Introduction to Formal Concept Analysis and Its Applications in Information Retrieval and Related Fields

  • Dmitry I. IgnatovEmail author
Part of the Communications in Computer and Information Science book series (CCIS, volume 505)


This paper is a tutorial on Formal Concept Analysis (FCA) and its applications. FCA is an applied branch of Lattice Theory, a mathematical discipline which enables formalisation of concepts as basic units of human thinking and analysing data in the object-attribute form. Originated in early 80s, during the last three decades, it became a popular human-centred tool for knowledge representation and data analysis with numerous applications. Since the tutorial was specially prepared for RuSSIR 2014, the covered FCA topics include Information Retrieval with a focus on visualisation aspects, Machine Learning, Data Mining and Knowledge Discovery, Text Mining and several others.


Formal Concept Analysis Concept lattices Information retrieval Machine learning Data mining Knowledge discovery Text mining Biclustering Multimodal clustering 



The author would like to thank all colleagues who have made this tutorial possible: Jaume Baixeries, Pavel Braslavsky, Peter Becker, Radim Belohlavek, Aliaksandr Birukou, Jean-Francois Boulicaut, Claudio Carpineto, Florent Domenach, Fritjhof Dau, Vincent Duquenne, Bernhard Ganter, Katja Hofmann, Robert Jaeshke, Evgenia Revne (Il’ina), Nikolay Karpov, Mehdy Kaytoue, Sergei Kuznetsov, Rokia Missaoui, Elena Nenova, Engelbert Mephu Nguifo, Alexei Neznanov, Lhouari Nourin, Bjoern Koester, Natalia Konstantinova, Amedeo Napoli, Sergei Obiedkov, Jonas Poelmans, Nikita Romashkin, Paolo Rosso, Sebastian Rudolph, Alexander Tuzhilin, Pavel Serdyukov, Baris Serkaya, Dominik Slezak, Marcin Szchuka, and, last but not least, the brave listeners. The author would also like to commemorate Ilya Segalovich who inspired the author’s enthusiasm in Information Retrieval studies, by giving personal explanations of near duplicate detection techniques in 2005, in particular.

Special thank should go to my grandmother, Vera, who has been hosting me in a peaceful countryside place, Prechistoe, during the last two weeks of the final preparations.

The author was partially supported by the Russian Foundation for Basic Research grants no. 13-07-00504 and 14-01-93960 and prepared the tutorial within the project “Data mining based on applied ontologies and lattices of closed descriptions” supported by the Basic Research Program of the National Research University Higher School of Economics.


  1. 1.
    Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)zbMATHGoogle Scholar
  2. 2.
    Wille, R.: Restructuring lattice theory: An approach based on hierarchies of concepts. In: Rival, I. (ed.) Ordered Sets. NATO Advanced Study Institutes Series, vol. 83, pp. 445–470. Springer, Heidelberg (1982)Google Scholar
  3. 3.
    Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer-Verlag New York Inc, Secaucus, NJ, USA (1999)zbMATHGoogle Scholar
  4. 4.
    Poelmans, J., Ignatov, D.I., Viaene, S., Dedene, G., Kuznetsov, S.O.: Text mining scientific papers: A survey on FCA-based information retrieval research. In: Perner, P. (ed.) ICDM 2012. LNCS, vol. 7377, pp. 273–287. Springer, Heidelberg (2012) Google Scholar
  5. 5.
    Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert Syst. Appl. 40(16), 6601–6623 (2013)Google Scholar
  6. 6.
    Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert Syst. Appl. 40(16), 6538–6560 (2013)Google Scholar
  7. 7.
    Serdyukov, P., Braslavski, P., Kuznetsov, S.O., Kamps, J., Rüger, S.M., Agichtein, E., Segalovich, I., Yilmaz, E. (eds.): Advances in Information Retrieval. LNCS, vol. 7814. Springer, Heidelberg (2013)Google Scholar
  8. 8.
    Arnauld, A., Nicole, P.: Logic or the Art of Thinking, translated by Jill V. Cambridge University Press, Buroker (1996)Google Scholar
  9. 9.
    Birkhoff, G.: Lattice Theory, 3rd edn. American Mathematical Society, Providence (1967) zbMATHGoogle Scholar
  10. 10.
    Ore, O.: Galois connexions. Trans. Amer. Math. Soc. 55(3), 494–513 (1944)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Barbut, M., Monjardet, B.: Ordre et Classification. Hachette, Paris (1970) zbMATHGoogle Scholar
  12. 12.
    Duquenne, V.: Latticial structures in data analysis. Theor. Comput. Sci. 217(2), 407–436 (1999)zbMATHGoogle Scholar
  13. 13.
    Wolski, M.: Galois connections and data analysis. Fundam. Inform. 60(1–4), 401–415 (2004)MathSciNetzbMATHGoogle Scholar
  14. 14.
    Kuznetsov, S.O.: Galois connections in data analysis: Contributions from the soviet era and modern russian research. In: Formal Concept Analysis, Foundations and Applications, pp. 196–225 (2005)Google Scholar
  15. 15.
    Carpineto, C., Romano, G.: Concept data analysis - theory and applications. Wiley, Chichester (2005)zbMATHGoogle Scholar
  16. 16.
    Davey, B.A., Priestley, H.A.: Introduction to Lattices and Order. Cambridge University Press, Cambridge (2002) zbMATHGoogle Scholar
  17. 17.
    Dominich, S.: The Modern Algebra of Information Retrieval, 1st edn. Springer Publishing Company, Heidelberg (2008). Incorporated zbMATHGoogle Scholar
  18. 18.
    Wolff, K.E.: A first course in formal concept analysis how to understand line diagrams. In: Faulbaum, F. (ed.), vol. 4 of SoftStat 1993. Advances in Statistical Software, pp. 429–438 (1993)Google Scholar
  19. 19.
    Belohlávek, R.: Introduction to Formal Concept Analysis. Palacky University, Olomouc (2008)Google Scholar
  20. 20.
    Kuznetsov, S.O., Obiedkov, S.A.: Comparing performance of algorithms for generating concept lattices. J. Exp. Theor. Artif. Intell. 14(2–3), 189–216 (2002)zbMATHGoogle Scholar
  21. 21.
    Kourie, D.G., Obiedkov, S.A., Watson, B.W., van der Merwe, D.: An incremental algorithm to construct a lattice of set intersections. Sci. Comput. Program. 74(3), 128–142 (2009)MathSciNetzbMATHGoogle Scholar
  22. 22.
    Krajca, P., Vychodil, V.: Distributed algorithm for computing formal concepts using Map-reduce framework. In: Siebes, A., Boulicaut, J.-F., Robardet, C., Adams, N.M. (eds.) IDA 2009. LNCS, vol. 5772, pp. 333–344. Springer, Heidelberg (2009) zbMATHGoogle Scholar
  23. 23.
    Xu, B., de Fréin, R., Robson, E., Ó Foghlú, M.: Distributed formal concept analysis algorithms based on an iterative MapReduce framework. In: Ignatov, D.I., Poelmans, J., Domenach, F. (eds.) ICFCA 2012. LNCS, vol. 7278, pp. 292–308. Springer, Heidelberg (2012) zbMATHGoogle Scholar
  24. 24.
    Armstrong, W.: Dependency structures of data base relationships. Inf. Process. 74, 580–583 (1974)MathSciNetzbMATHGoogle Scholar
  25. 25.
    Maier, D.: The Theory of Relational Databases. Computer Science Press, Rockville (1983)zbMATHGoogle Scholar
  26. 26.
    Guigues, J.L., Duquenne, V.: Familles minimales d’implications informatives rsultant d’un tableau de donnes binaires. Math. et Sci. Humaines 95(1), 5–18 (1986). In FrenchGoogle Scholar
  27. 27.
    Bazhanov, K., Obiedkov, S.A.: Optimizations in computing the duquenne-guigues basis of implications. Ann. Math. Artif. Intell. 70(1–2), 5–24 (2014)MathSciNetzbMATHGoogle Scholar
  28. 28.
    Baixeries, J., Kaytoue, M., Napoli, A.: Characterization of database dependencies with FCA and pattern structures. In: Ignatov, D.I., Khachay, M.Y., Panchenko, A., Konstantinova, N., Yavorsky, R.E. (eds.) AIST 2014. CCIS, vol. 436, pp. 3–14. Springer, Heidelberg (2014) zbMATHGoogle Scholar
  29. 29.
    Yevtushenko, S.A.: System of data analysis “concept explorer”. (in russian). In: Proceedings of the 7th National Conference on Artificial Intelligence KII-2000, pp. 127–134 (2000)Google Scholar
  30. 30.
    Yevtushenko, S.: Computing and Visualizing Concept Lattices. Ph.D. thesis, TU Darmstadt, Fachbereich Informatik (2004)Google Scholar
  31. 31.
    Yevtushenko, S.A.: Concept Explorer. The User Guide, September 12 2006Google Scholar
  32. 32.
    Becker, P.: Numerical analysis in conceptual systems with ToscanaJ. In: Eklund, P. (ed.) ICFCA 2004. LNCS (LNAI), vol. 2961, pp. 96–103. Springer, Heidelberg (2004) Google Scholar
  33. 33.
    Becker, P., Correia, J.H.: The toscanaj suite for implementing conceptual information systems. In: Formal Concept Analysis, Foundations and Applications, pp. 324–348 (2005)Google Scholar
  34. 34.
    Vogt, F., Wille, R.: TOSCANA – A graphical tool for analyzing and exploring data. In: Tamassia, R., Tollis, I.G. (eds.) Graph Drawing. LNCS, vol. 894, pp. 226–233. Springer, Heidelberg (1995)Google Scholar
  35. 35.
    Valtchev, P., Grosser, D., Roume, C., Hacene, M.R.: Galicia: an open platform for lattices. In: de Moor, A., Ganter, B., (ed.), Using Conceptual Structures: Contributions to 11th International Conference on Conceptual Structures, pp. 241–254 (2003)Google Scholar
  36. 36.
    Lahcen, B., Kwuida., L.: Lattice miner: A tool for concept lattice construction and exploration. In: Suplementary Proceeding of International Conference on Formal Concept Analysis (ICFCA 2010) (2010)Google Scholar
  37. 37.
    Poelmans, J., Elzinga, P., Ignatov, D.I., Kuznetsov, S.O.: Semi-automated knowledge discovery: identifying and profiling human trafficking. Int. J. Gen. Syst. 41(8), 774–804 (2012)MathSciNetGoogle Scholar
  38. 38.
    Poelmans, J., Elzinga, P., Neznanov, A., Viaene, S., Kuznetsov, S., Ignatov, D., Dedene, G.: Concept relation discovery and innovation enabling technology (cordiet). In: Proceedings of 1st International Workshop on Concept Discovery in Unstructured Data. vol. 757 of CEUR Workshop proceedings (2011)Google Scholar
  39. 39.
    Neznanov, A., Ilvovsky, D., Kuznetsov, S.O.: Fcart: A new fca-based system for data analysis and knowledge discovery. In: Contributions to the 11th International Conference on Formal Concept Analysis, TU Dresden, pp. 31–44 (2013)Google Scholar
  40. 40.
    Neznanov, A.A., Parinov, A.A.: FCA analyst session and data access tools in FCART. In: Agre, G., Hitzler, P., Krisnadhi, A.A., Kuznetsov, S.O. (eds.) AIMSA 2014. LNCS, vol. 8722, pp. 214–221. Springer, Heidelberg (2014) Google Scholar
  41. 41.
    Buzmakov, A., Neznanov, A.: Practical computing with pattern structures in FCART environment. In: Proceedings of the International Workshop “What can FCA do for Artificial Intelligence?” (FCA4AI at IJCAI 2013), pp. 49–56. Beijing, China, August 5 2013Google Scholar
  42. 42.
    Domenach, F.: CryptoLat - a pedagogical software on lattice cryptomorphisms and lattice properties. In: Proceedings of the Tenth International Conference on Concept Lattices and Their Applications, La Rochelle, France, October 15–18, 2013, pp. 93–103 (2013)Google Scholar
  43. 43.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.): VLDB, Morgan Kaufmann, pp. 487–499 (1994)Google Scholar
  44. 44.
    Luxenburger, M.: Implications partielles dans un contexte. Mathématiques, Informatique et Sci. Humaines 29(113), 35–55 (1991)MathSciNetzbMATHGoogle Scholar
  45. 45.
    Jäschke, R., Hotho, A., Schmitz, C., Ganter, B., Stumme, G.: Trias-an algorithm for mining iceberg tri-lattices. In: Proceedings of the Sixth International Conference on Data Mining. ICDM 2006, pp. 907–911. IEEE Computer Society, Washington, DC, USA (2006)Google Scholar
  46. 46.
    Ignatov, D.I., Kuznetsov, S.O., Magizov, R.A., Zhukov, L.E.: From triconcepts to triclusters, vol. 247 257–264Google Scholar
  47. 47.
    Ignatov, D.I., Kuznetsov, S.O., Poelmans, J., Zhukov, L.E.: Can triconcepts become triclusters? Int. J. Gen. Syst. 42(6), 572–593 (2013)MathSciNetzbMATHGoogle Scholar
  48. 48.
    Kuznetsov, S.O.: Machine learning and formal concept analysis, vol. 248, 287–312Google Scholar
  49. 49.
    Ganter, B., Grigoriev, P.A., Kuznetsov, S.O., Samokhin, M.V.: Concept-based data mining with scaled labeled graphs. In: Delugach, H.S., Wolff, K.E., Pfeiffer, H.D. (eds.) ICCS 2004. LNCS (LNAI), vol. 3127, pp. 94–108. Springer, Heidelberg (2004) Google Scholar
  50. 50.
    Kuznetsov, S.O.: Fitting pattern structures to knowledge discovery in big data. In: Cellier, P., Distel, F., Ganter, B. (eds.) ICFCA 2013. LNCS, vol. 7880, pp. 254–266. Springer, Heidelberg (2013) Google Scholar
  51. 51.
    Belohlávek, R., Vychodil, V.: Discovery of optimal factors in binary data via a novel method of matrix decomposition. J. Comput. Syst. Sci. 76(1), 3–20 (2010)MathSciNetzbMATHGoogle Scholar
  52. 52.
    Romashkin, N., Ignatov, D.I., Kolotova, E.: How university entrants are choosing their department? mining of university admission process with FCA taxonomies. In: Proceedings of the 4th International Conference on Educational Data Mining, Eindhoven, The Netherlands, July 6–8, 2011, pp. 229–234 (2011)Google Scholar
  53. 53.
    Grigoriev, P.A., Yevtushenko, S.A.: Quda: Applying formal concept analysis in a data mining environment. vol. 248, pp. 386–393Google Scholar
  54. 54.
    Han, J., Kamber, M.: Data Mining Concepts and Techniques. Morgan Kaufmann, San Francisco (2000) zbMATHGoogle Scholar
  55. 55.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. ACM SIGMOD Rec. 22(2), 207–216 (1993). ACMGoogle Scholar
  56. 56.
    Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Efficient mining of association rules using closed itemset lattices. Inf. Syst. 24(1), 25–46 (1999)zbMATHGoogle Scholar
  57. 57.
    Zaki, M.J., Hsiao, C.J.: Charm: An efficient algorithm for closed association rule mining. Technical Report, Computer Science, Rensselaer Polytechnic Institute (1999)Google Scholar
  58. 58.
    Stumme, G.: Conceptual knowledge discovery with frequent concept lattices. Technical Report FB4- Preprint 2043, TU Darmstadt (1999)Google Scholar
  59. 59.
    Stumme, G., Taouil, R., Bastide, Y., Pasquier, N., Lakhal, L.: Computing iceberg concept lattices with T. Data Knowl. Eng. 42(2), 189–222 (2002)zbMATHGoogle Scholar
  60. 60.
    Kuznetsov, S.: Mathematical aspects of concept analysis. J. Math. Sci. 80(2), 1654–1698 (1996)MathSciNetzbMATHGoogle Scholar
  61. 61.
    Lakhal, L., Stumme, G.: Efficient mining of association rules based on formal concept analysis. In: Formal Concept Analysis, Foundations and Applications, pp. 180–195 (2005)Google Scholar
  62. 62.
    Agrawal, R., Christoforaki, M., Gollapudi, S., Kannan, A., Kenthapadi, K., Swaminathan, A.: Mining videos from the web for electronic textbooks. In: Formal Concept Analysis - 12th International Conference, ICFCA 2014, Cluj-Napoca, Romania, June 10–13, 2014. Proceedings, pp. 219–234 (2014)Google Scholar
  63. 63.
    Zaki, M.J., Wagner Meira, J.: Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press, Cambridge (2014) zbMATHGoogle Scholar
  64. 64.
    Zaki, M.J.: Spade: An efficient algorithm for mining frequent sequences. Mach. Learn. 42, 31–60 (2001)zbMATHGoogle Scholar
  65. 65.
    Vander Wal, T.: Folksonomy coinage and definition. (2007). Accessed on 12.03.2012
  66. 66.
    Mirkin, B.: Math. Classif. Clustering. Kluwer, Dordrecht (1996) Google Scholar
  67. 67.
    Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: A survey. IEEE/ACM Trans. Comput. Biology Bioinform. 1(1), 24–45 (2004)Google Scholar
  68. 68.
    Eren, K., Deveci, M., Kktun, O., atalyrek, M.V.: A comparative analysis of biclustering algorithms for gene expression data. Briefings in Bioinformatics (2012)Google Scholar
  69. 69.
    Besson, J., Robardet, C., Boulicaut, J.F., Rome, S.: Constraint-based concept mining and its application to microarray data analysis. Intell. Data Anal. 9(1), 59–82 (2005)Google Scholar
  70. 70.
    Barkow, S., Bleuler, S., Prelic, A., Zimmermann, P., Zitzler, E.: Bicat: a biclustering analysis toolbox. Bioinformatics 22(10), 1282–1283 (2006)Google Scholar
  71. 71.
    Tarca, A.L., Carey, V.J., wen Chen, X., Romero, R., Drǎghici, S.: Machine learning and its applications to biology. PLoS Comput. Biol. 3(6), e116 (2007)Google Scholar
  72. 72.
    Hanczar, B., Nadif, M.: Bagging for biclustering: application to microarray data. In: Sebag, M., Balcázar, J.L., Gionis, A., Bonchi, F. (eds.) ECML PKDD 2010, Part I. LNCS, vol. 6321, pp. 490–505. Springer, Heidelberg (2010) Google Scholar
  73. 73.
    Kaytoue, M., Kuznetsov, S.O., Napoli, A., Duplessis, S.: Mining gene expression data with pattern structures in formal concept analysis. Inf. Sci. 181(10), 1989–2001 (2011)MathSciNetGoogle Scholar
  74. 74.
    Blinova, V.G., Dobrynin, D.A., Finn, V.K., Kuznetsov, S.O., Pankratova, E.S.: Toxicology analysis by means of the jsm-method. Bioinformatics 19(10), 1201–1207 (2003)Google Scholar
  75. 75.
    Kuznetsov, S.O., Samokhin, M.V.: Learning closed sets of labeled graphs for chemical applications. In: Kramer, S., Pfahringer, B. (eds.) ILP 2005. LNCS (LNAI), vol. 3625, pp. 190–208. Springer, Heidelberg (2005) Google Scholar
  76. 76.
    DiMaggio, P.A., Subramani, A., Judson, R.S., Floudas, C.A.: A novel framework for predicting in vivo toxicities from in vitro data using optimal methods for dense and sparse matrix reordering and logistic regression. Toxicol. Sci. 118(1), 251–265 (2010)Google Scholar
  77. 77.
    Asses, Y., Buzmakov, A., Bourquard, T., Kuznetsov, S.O., Napoli, A.: A hybrid classification approach based on FCA and emerging patterns - an application for the classification of biological inhibitors. In: Proceedings of The 9th International Conference on Concept Lattices and Their Applications, pp. 211–222 (2012)Google Scholar
  78. 78.
    Dhillon, I.S.: Co-clustering documents and words using bipartite spectral graph partitioning. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD 2001, New York, NY, USA, pp. 269–274. ACM (2001)Google Scholar
  79. 79.
    Cimiano, P., Hotho, A., Staab, S.: Learning concept hierarchies from text corpora using formal concept analysis. J. Artif. Intell. Res. (JAIR) 24, 305–339 (2005)zbMATHGoogle Scholar
  80. 80.
    Banerjee, A., Dhillon, I.S., Ghosh, J., Merugu, S., Modha, D.S.: A generalized maximum entropy approach to bregman co-clustering and matrix approximation. J. Mach. Learn. Res. 8, 1919–1986 (2007)MathSciNetzbMATHGoogle Scholar
  81. 81.
    Ignatov, D.I., Kuznetsov, S.O.: Frequent itemset mining for clustering near duplicate web documents. [249] 185–200Google Scholar
  82. 82.
    Carpineto, C., Michini, C., Nicolussi, R.: A concept lattice-based kernel for SVM text classification. In: Rudolph, S., Ferré, S. (eds.) ICFCA 2009. LNCS, vol. 5548, pp. 237–250. Springer, Heidelberg (2009) Google Scholar
  83. 83.
    Koester, B.: Conceptual knowledge retrieval with FooCA: improving web search engine results with contexts and concept hierarchies. In: Perner, P. (ed.) ICDM 2006. LNCS (LNAI), vol. 4065, pp. 176–190. Springer, Heidelberg (2006) Google Scholar
  84. 84.
    Eklund, P.W., Ducrou, J., Dau, F.: Concept similarity and related categories in information retrieval using formal concept analysis. Int. J. Gen. Syst. 41(8), 826–846 (2012)MathSciNetGoogle Scholar
  85. 85.
    Duquenne, V.: Lattice analysis and the representation of handicap associations. Soc. Netw. 18(3), 217–230 (1996)Google Scholar
  86. 86.
    Freeman, L.C.: Cliques, Galois lattices, and the structure of human social groups. Soc. Netw. 18, 173–187 (1996)Google Scholar
  87. 87.
    Latapy, M., Magnien, C., Vecchio, N.D.: Basic notions for the analysis of large two-mode networks. Soc. Netw. 30(1), 31–48 (2008)Google Scholar
  88. 88.
    Roth, C., Obiedkov, S.A., Kourie, D.G.: On succinct representation of knowledge community taxonomies with formal concept analysis. Int. J. Found. Comput. Sci. 19(2), 383–404 (2008)MathSciNetzbMATHGoogle Scholar
  89. 89.
    Gnatyshak, D., Ignatov, D.I., Semenov, A., Poelmans, J.: Gaining insight in social networks with biclustering and triclustering. In: Aseeva, N., Babkin, E., Kozyrev, O. (eds.) BIR 2012. LNBIP, vol. 128, pp. 162–171. Springer, Heidelberg (2012) Google Scholar
  90. 90.
    du Boucher-Ryan, P., Bridge, D.G.: Collaborative recommending using formal concept analysis. Knowl. Based Syst. 19(5), 309–315 (2006)Google Scholar
  91. 91.
    Symeonidis, P., Nanopoulos, A., Papadopoulos, A.N., Manolopoulos, Y.: Nearest-biclusters collaborative filtering based on constant and coherent values. Inf. Retr. 11(1), 51–75 (2008)Google Scholar
  92. 92.
    Ignatov, D.I., Kuznetsov, S.O.: Concept-based recommendations for internet advertisement. In: Belohlavek, R., Kuznetsov, S.O. (eds.): Proc. CLA 2008. Vol. 433 of CEUR WS., Palack University, Olomouc, 2008, pp. 157–166 (2008)Google Scholar
  93. 93.
    Nanopoulos, A., Rafailidis, D., Symeonidis, P., Manolopoulos, Y.: Musicbox: personalized music recommendation based on cubic analysis of social tags. IEEE Trans. Audio, Speech Lang. Process. 18(2), 407–412 (2010)Google Scholar
  94. 94.
    Ignatov, D.I., Nenova, E., Konstantinova, N., Konstantinov, A.V.: Boolean matrix factorisation for collaborative filtering: An FCA-based approach. In: Agre, G., Hitzler, P., Krisnadhi, A.A., Kuznetsov, S.O. (eds.) AIMSA 2014. LNCS, vol. 8722, pp. 47–58. Springer, Heidelberg (2014) Google Scholar
  95. 95.
    Ignatov, D.I.: Mathematical Models, Algorithms and Software Tools of Biclustering Based on Closed Sets. Ph.D. thesis, National Research University Higher School of Economics (2010)Google Scholar
  96. 96.
    Ignatov, D.I., Kuznetsov, S.O., Poelmans, J.: Concept-based biclustering for internet advertisement. In: ICDM Workshops, IEEE Computer Society, 123–130 (2012)Google Scholar
  97. 97.
    Benz, D., Hotho, A., Jäschke, R., Krause, B., Mitzlaff, F., Schmitz, C., Stumme, G.: The social bookmark and publication management system bibsonomy - A platform for evaluating and demonstrating web 2.0 research. VLDB J. 19(6), 849–875 (2010)Google Scholar
  98. 98.
    Zhao, L., Zaki, M.J.: Tricluster: An effective algorithm for mining coherent clusters in 3D microarray data. In: Özcan, F. (ed.): SIGMOD Conference, pp. 694–705. ACM (2005)Google Scholar
  99. 99.
    Li, A., Tuck, D.: An effective tri-clustering algorithm combining expression data with gene regulation information. Gene Regul. Syst. Biol. 3, 49–64 (2009)Google Scholar
  100. 100.
    Wille, R.: The basic theorem of triadic concept analysis. Order 12, 149–158 (1995)MathSciNetzbMATHGoogle Scholar
  101. 101.
    Lehmann, F., Wille, R.: A triadic approach to formal concept analysis. In: Ellis, G., Levinson, R., Rich, W., Sowa, J.F. (eds.) Conceptual Structures: Applications, Implementation and Theory. LNCS, vol. 954, pp. 32–43. Springer, Heidelberg (1995) Google Scholar
  102. 102.
    Krolak-Schwerdt, S., Orlik, P., Ganter, B.: Tripat: a model for analyzing three-mode binary data. In Bock, H.H., Lenski, W., Richter, M. (eds.): Information Systems and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin Heidelberg, pp. 298–307 (1994)Google Scholar
  103. 103.
    Ji, L., Tan, K.L., Tung, A.K.H.: Mining frequent closed cubes in 3d datasets. In: Proceedings of the 32nd International Conference on Very Large Data Bases. VLDB 2006, VLDB Endowment, pp. 811–822 (2006)Google Scholar
  104. 104.
    Cerf, L., Besson, J., Robardet, C., Boulicaut, J.F.: Closed patterns meet n-ary relations. ACM Trans. Knowl. Discov. Data 3, 3:1–3:36 (2009)Google Scholar
  105. 105.
    Cerf, L., Besson, J., Nguyen, K.N., Boulicaut, J.F.: Closed and noise-tolerant patterns in n-ary relations. Data Min. Knowl. Discov. 26(3), 574–619 (2013)MathSciNetzbMATHGoogle Scholar
  106. 106.
    Georgii, E., Tsuda, K., Schölkopf, B.: Multi-way set enumeration in weight tensors. Mach. Learn. 82(2), 123–155 (2011)MathSciNetzbMATHGoogle Scholar
  107. 107.
    Spyropoulou, E., De Bie, T., Boley, M.: Interesting pattern mining in multi-relational data. Data Min. Knowl. Disc. 28(3), 808–849 (2014)MathSciNetzbMATHGoogle Scholar
  108. 108.
    Voutsadakis, G.: Polyadic concept analysis. Order 19(3), 295–304 (2002)MathSciNetzbMATHGoogle Scholar
  109. 109.
    Ignatov, D., Gnatyshak, D., Kuznetsov, S., Mirkin, B.: Triadic formal concept analysis and triclustering: searching for optimal patterns. Mach. Learn. 42, 1–32 (2015)MathSciNetzbMATHGoogle Scholar
  110. 110.
    Mirkin, B., Kramarenko, A.V.: Approximate bicluster and tricluster boxes in the analysis of binary data. [247] 248–256Google Scholar
  111. 111.
    Gnatyshak, D., Ignatov, D.I., Kuznetsov, S.O.: From triadic fca to triclustering: Experimental comparison of some triclustering algorithms. [250] 249–260Google Scholar
  112. 112.
    Gnatyshak, D.V., Ignatov, D.I., Kuznetsov, S.O., Nourine, L.: A one-pass triclustering approach: Is there any room for big data? In: CLA 2014 (2014)Google Scholar
  113. 113.
    Ganter, B., Kuznetsov, S.O.: Hypotheses and version spaces. In: Ganter, B., de Moor, A., Lex, W. (eds.) Conceptual Structures for Knowledge Creation and Communication. LNCS, vol. 2746, pp. 83–95. Springer, Heidelberg (2003) Google Scholar
  114. 114.
    Belohlávek, R., Baets, B.D., Outrata, J., Vychodil, V.: Inducing decision trees via concept lattices. Int. J. Gen. Syst. 38(4), 455–467 (2009)MathSciNetzbMATHGoogle Scholar
  115. 115.
    Carpineto, C., Romano, G.: Galois: An order-theoretic approach to conceptual clustering. In: Proceeding of ICML93, Amherst, pp. 33–40 (1993)Google Scholar
  116. 116.
    Carpineto, C., Romano, G.: A lattice conceptual clustering system and its application to browsing retrieval. Mach. Learn. 24, 95–122 (1996)Google Scholar
  117. 117.
    Fu, H., Fu, H., Njiwoua, P., Nguifo, E.M.: A comparative study of FCA-based supervised classification algorithms. In: Eklund, P. (ed.) ICFCA 2004. LNCS (LNAI), vol. 2961, pp. 313–320. Springer, Heidelberg (2004) Google Scholar
  118. 118.
    Rudolph, S.: Using FCA for encoding closure operators into neural networks. In: Proceedings of the 15th International Conference on Conceptual Structures, ICCS 2007, Sheffield, UK, July 22–27, 2007, pp. 321–332 (2007)Google Scholar
  119. 119.
    Tsopzé, N., Nguifo, E.M., Tindo, G.: CLANN: concept lattice-based artificial neural network for supervised classification. In: Proceedings of the 5th International Conference on Concept Lattices and Their Applications, CLA 2007 (2007)Google Scholar
  120. 120.
    Outrata, J.: Boolean factor analysis for data preprocessing in machine learning. In: The Ninth International Conference on Machine Learning and Applications, ICMLA 2010, Washington, DC, USA, 12–14 December 2010, pp. 899–902 (2010)Google Scholar
  121. 121.
    Belohlávek, R., Outrata, J., Trnecka, M.: Impact of boolean factorization as preprocessing methods for classification of boolean data. Ann. Math. Artif. Intell. 72(1–2), 3–22 (2014)MathSciNetzbMATHGoogle Scholar
  122. 122.
    Ganter, B., Kuznetsov, S.O.: Scale coarsening as feature selection. In: Medina, R., Obiedkov, S. (eds.) Formal Concept Analysis. LNCS, vol. 4933, pp. 217–228. Springer, Heidelberg (2008) Google Scholar
  123. 123.
    Visani, M., Bertet, K., Ogier, J.: Navigala: an original symbol classifier based on navigation through a Galois lattice. IJPRAI 25(4), 449–473 (2011)MathSciNetGoogle Scholar
  124. 124.
    Zaki, M.J., Aggarwal, C.C.: Xrules: An effective algorithm for structural classification of XML data. Mach. Learn. 62(1–2), 137–170 (2006)Google Scholar
  125. 125.
    Flach, P.: Machine Learning: The Art and Science of Algorithms That Make Sense of Data. Cambridge University Press, New York (2012) zbMATHGoogle Scholar
  126. 126.
    Finn, V.: On machine-oriented formalization of plausible reasoning in f.bacon-j.s.mill style. Semiotika i Informatika 20, 35–101 (1983). (in Russian)Google Scholar
  127. 127.
    Kuznetsov, S.: Jsm-method as a machine learning. Method. Itogi Nauki i Tekhniki, ser. Informatika 15, 17–52 (1991). (in Russian)Google Scholar
  128. 128.
    Gusakova, S.: Paleography with jsm-method. Technical Report, VINITI (2001)Google Scholar
  129. 129.
    Ganter, B., Kuznetsov, S.: Formalizing hypotheses with concepts. In: Ganter, B., Mineau, G. (eds.) Conceptual Structures: Logical, Linguistic, and Computational Issues. Lecture Notes in Computer Science, vol. 1867, pp. 342–356. Springer, Heidelberg (2000)Google Scholar
  130. 130.
    Zhuk, R., Ignatov, D.I., Konstantinova, N.: Concept learning from triadic data. In: Proceedings of the Second International Conference on Information Technology and Quantitative Management, ITQM 2014, National Research University Higher School of Economics (HSE), Moscow, Russia, June 3–5, 2014, pp. 928–938 (2014)Google Scholar
  131. 131.
    Ignatov, D.I., Zhuk, R., Konstantinova, N.: Learning hypotheses from triadic labeled data. In: 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), Warsaw, Poland, August 11–14, 2014 - vol. I, pp. 474–480 (2014)Google Scholar
  132. 132.
    Ganter, B., Kuznetsov, S.O.: Pattern structures and their projections. In: Delugach, H.S., Stumme, G. (eds.) ICCS 2001. LNCS (LNAI), vol. 2120, pp. 129–142. Springer, Heidelberg (2001) Google Scholar
  133. 133.
    Buzmakov, A., Egho, E., Jay, N., Kuznetsov, S.O., Napoli, A., Raïssi, C.: On projections of sequential pattern structures (with an application on care trajectories). [250] 199–208Google Scholar
  134. 134.
    Kuznetsov, S.O.: Scalable knowledge discovery in complex data with pattern structures. In: Maji, P., Ghosh, A., Murty, M.N., Ghosh, K., Pal, S.K. (eds.) PReMI 2013. LNCS, vol. 8251, pp. 30–39. Springer, Heidelberg (2013) Google Scholar
  135. 135.
    Strok, F., Galitsky, B., Ilvovsky, D., Kuznetsov, S.: Pattern structure projections for learning discourse structures. In: Agre, G., Hitzler, P., Krisnadhi, A.A., Kuznetsov, S.O. (eds.) AIMSA 2014. LNCS, vol. 8722, pp. 254–260. Springer, Heidelberg (2014) Google Scholar
  136. 136.
    Belohlávek, R.: What is a fuzzy concept lattice? II. [247] 19–26Google Scholar
  137. 137.
    Kent, R.E.: Rough concept analysis: A synthesis of rough sets and formal concept analysis. Fundam. Inform. 27(2/3), 169–181 (1996)MathSciNetzbMATHGoogle Scholar
  138. 138.
    Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Fuzzy and rough formal concept analysis: a survey. Int. J. Gen. Syst. 43(2), 105–134 (2014)MathSciNetzbMATHGoogle Scholar
  139. 139.
    Pankratieva, V.V., Kuznetsov, S.O.: Relations between proto-fuzzy concepts, crisply generated fuzzy concepts, and interval pattern structures. Fundam. Inform. 115(4), 265–277 (2012)MathSciNetzbMATHGoogle Scholar
  140. 140.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)Google Scholar
  141. 141.
    Elden, L.: Matrix methods in data mining and pattern recognition. In: Society for Industrial and Applied Mathematics (2007).
  142. 142.
    Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Mach. Learn. 42(1–2), 177–196 (2001)zbMATHGoogle Scholar
  143. 143.
    Koren, Y.: Factorization meets the neighborhood: A multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD 2008, pp. 426–434. New York, NY, USA, ACM (2008)Google Scholar
  144. 144.
    Lin, C.J.: Projected gradient methods for nonnegative matrix factorization. Neural Comput. 19(10), 2756–2779 (2007)MathSciNetzbMATHGoogle Scholar
  145. 145.
    Nenova, E., Ignatov, D.I., Konstantinov, A.V.: An fca-based boolean matrix factorisation for collaborative filtering. In: International Workshop FCA meets IR at ECIR 2013. vol. 977, CEUR Workshop Proceeding, pp. 57–73 (2013)Google Scholar
  146. 146.
    Belohlávek, R., Glodeanu, C., Vychodil, V.: Optimal factorization of three-way binary data using triadic concepts. Order 30(2), 437–454 (2013)MathSciNetzbMATHGoogle Scholar
  147. 147.
    Miettinen, P.: Boolean tensor factorization. In: Cook, D., Pei, J., Wang, W., Zaïane, O., Wu, X. (eds.) ICDM 2011, 11th IEEE International Conference on Data Mining, pp. 447–456. Canada, IEEE Computer Society, CPS, Vancouver (2011)Google Scholar
  148. 148.
    Hotho, A., Jäschke, R., Schmitz, C., Stumme, G.: Information retrieval in folksonomies: search and ranking. In: Domingue, J., Sure, Y. (eds.) ESWC 2006. LNCS, vol. 4011, pp. 411–426. Springer, Heidelberg (2006) Google Scholar
  149. 149.
    Zhiltsov, N., Agichtein, E.: Improving entity search over linked data by modeling latentsemantics. In: 22nd ACM International Conference on Information and Knowledge Management, CIKM 2013, San Francisco, CA, USA, October 27 - November 1, 2013, pp. 1253–1256 (2013)Google Scholar
  150. 150.
    Ignatov, D.I., Mamedova, S., Romashkin, N., Shamshurin, I.: What can closed sets of students and their marks say? In: Proceedings of the 4th International Conference on Educational Data Mining, Eindhoven, The Netherlands, July 6–8, 2011, pp. 223–228 (2011)Google Scholar
  151. 151.
    Grigoriev, P., Yevtushenko, S., Grieser, G.: QuDA, a Data Miners Discovery Environment. Technical Report AIDA-03-06, Technische Universität Darmstadt (2003)Google Scholar
  152. 152.
    Grigoriev, P.A., Yevtushenko, S.A.: Elements of an agile discovery environment. In: Grieser, G., Tanaka, Y., Yamamoto, A. (eds.) Discovery Science. LNCS, vol. 2843, pp. 311–319. Springer, Heidelberg (2003)Google Scholar
  153. 153.
    Grigoriev, P., Kuznetsov, S., Obiedkov, S., Yevtushenko, S.: On a version of mill’s method of difference. In: Proceedings of the ECAI 2002 Workshop on Concept Lattices in Data Mining, Lyon, pp. 26–31 (2002)Google Scholar
  154. 154.
    Mooers, C.N.: A mathematical theory of language symbols in retrieval. In: Proceedings of the International Conference Scientific Information, Washington D.C. (1958)Google Scholar
  155. 155.
    Fairthorne, R.A.: The patterns of retrieval. Am. Documentation 7(2), 65–70 (1956)Google Scholar
  156. 156.
    Shreider, Y.: Mathematical model of classification theory, pp. 1–36. VINITI, Moscow (1968). (in Russian)Google Scholar
  157. 157.
    Soergel, D.: Mathematical analysis of documentation systems. Inf. Stor. Retr. 3, 129–173 (1967)Google Scholar
  158. 158.
    Godin, R., Saunders, E., Gecsei, J.: Lattice model of browsable data spaces. Inf. Sci. 40(2), 89–116 (1986)zbMATHGoogle Scholar
  159. 159.
    Carpineto, C., Romano, G.: Using concept lattices for text retrieval and mining. In: Formal Concept Analysis, Foundations and Applications, pp. 161–179 (2005)Google Scholar
  160. 160.
    Priss, U.: Formal concept analysis in information science. ARIST 40(1), 521–543 (2006)Google Scholar
  161. 161.
    Valverde-Albacete, F.J., Pelaez-Moreno, C.: Systems vs. methods: an analysis of the affordances of formal concept analysis for information retrieval? In: Proceedings of the of International Workshop on FCA for IR at ECIR 2013, HSE, Moscow (2013)Google Scholar
  162. 162.
    Ferr, S.: Camelis: Organizing and browsing a personal photo collection with a logical information system. In: Eklund, P.W., Diatta, J., Liquiere, M. (eds.): CLA. vol. 331 of CEUR Workshop Proceedings. (2007)Google Scholar
  163. 163.
    Ignatov, D.I., Konstantinov, A.V., Chubis, Y.: Near-duplicate detection for online-shops owners: An fca-based approach. [7] 722–725Google Scholar
  164. 164.
    Kuznetsov, S.O., Ignatov, D.I.: Concept stability for constructing taxonomies of web-site users. In: Obiedkov, S., Roth, C. (eds.) Proceedings of the Social Network Analysis and Conceptual Structures: Exploring Opportunities, Clermont-Ferrand (France), February 16, 2007 (2007)Google Scholar
  165. 165.
    Ignatov, D.I., Kaminskaya, A.Y., Bezzubtseva, A.A., Konstantinov, A.V., Poelmans, J.: FCA-based models and a prototype data analysis system for crowdsourcing platforms. In: Pfeiffer, H.D., Ignatov, D.I., Poelmans, J., Gadiraju, N. (eds.) ICCS 2013. LNCS, vol. 7735, pp. 173–192. Springer, Heidelberg (2013) Google Scholar
  166. 166.
    Carpineto, C., Romano, G.: A survey of automatic query expansion in information retrieval. ACM Comput. Surv. 44(1), 1 (2012)zbMATHGoogle Scholar
  167. 167.
    Carpineto, C., Romano, G.: Order-theoretical ranking. JASIS 51(7), 587–601 (2000)Google Scholar
  168. 168.
    Carpineto, C., Osinski, S., Romano, G., Weiss, D.: A survey of web clustering engines. ACM Comput. Surv. 41(3), 1–38 (2009)Google Scholar
  169. 169.
    Carpineto, C., Romano, G.: Exploiting the potential of concept lattices for information retrieval with CREDO. J. UCS 10(8), 985–1013 (2004)zbMATHGoogle Scholar
  170. 170.
    Ducrou, J., Eklund, P.W.: Searchsleuth: The conceptual neighbourhood of an web query. In: Proceedings of the Fifth International Conference on Concept Lattices and Their Applications, CLA 2007, Montpellier, France, October 24–26, 2007 (2007)Google Scholar
  171. 171.
    Dau, F., Ducrou, J., Eklund, P.: Concept similarity and related categories in searchsleuth. In: Eklund, P., Haemmerlé, O. (eds.) ICCS 2008. LNCS (LNAI), vol. 5113, pp. 255–268. Springer, Heidelberg (2008) Google Scholar
  172. 172.
    Nauer, E., Toussaint, Y.: Crechaindo: an iterative and interactive web information retrieval system based on lattices. Int. J. Gen. Syst. 38(4), 363–378 (2009)zbMATHGoogle Scholar
  173. 173.
    Kim, M., Compton, P.: Evolutionary document management and retrieval for specialized domains on the web. Int. J. Hum.-Comput. Stud. 60(2), 201–241 (2004)Google Scholar
  174. 174.
    Kim, M.H., Compton, P.: A hybrid browsing mechanism using conceptual scales. In: Richards, D., Hoffmann, A., Tsumoto, S., Kang, B.-H. (eds.) PKAW 2006. LNCS (LNAI), vol. 4303, pp. 132–143. Springer, Heidelberg (2006) Google Scholar
  175. 175.
    Cigarrán, J.M., Gonzalo, J., Peñas, A., Verdejo, F.: Browsing search results via formal concept analysis: Automatic selection of attributes. [248] 74–87Google Scholar
  176. 176.
    Cole, R.J., Eklund, P.W., Stumme, G.: Document retrieval for e-mail search and discovery using formal concept analysis. Appl. Artif. Intell. 17(3), 257–280 (2003)Google Scholar
  177. 177.
    Cole, R.J., Eklund, P.W.: Browsing semi-structured web texts using formal concept analysis. In: Conceptual Structures: Broadening the Base, 9th International Conference on Conceptual Structures, ICCS 2001, Stanford, CA, USA, July 30-August 3, 2001, Proceedings, pp. 319–332 (2001)Google Scholar
  178. 178.
    Eklund, P.W., Cole, R.J.: A knowledge representation for information filtering using formal concept analysis. Electron. Trans. Artif. Intell. 4(C), 51–51 (2000)Google Scholar
  179. 179.
    Eklund, P.W., Ducrou, J., Brawn, P.: Concept lattices for information visualization: Can novices read line-diagrams? [248] 57–73Google Scholar
  180. 180.
    Eklund, P., Wormuth, B.: Restructuring help systems using formal concept analysis. In: Godin, R., Ganter, B. (eds.) ICFCA 2005. LNCS (LNAI), vol. 3403, pp. 129–144. Springer, Heidelberg (2005) zbMATHGoogle Scholar
  181. 181.
    Stojanovic, N.: On the query refinement in the ontology-based searching for information. Inf. Syst. 30(7), 543–563 (2005)Google Scholar
  182. 182.
    Spyratos, N., Meghini, C.: Preference-based query tuning through refinement/enlargement in a formal context. In: Hegner, S.J., Dix, J. (eds.) FoIKS 2006. LNCS, vol. 3861, pp. 278–293. Springer, Heidelberg (2006) Google Scholar
  183. 183.
    Le Grand, B., Aufaure, M.-A., Soto, M.: Semantic and conceptual context-aware information retrieval. In: Yetongnon, K., Chbeir, R., Damiani, E., Dipanda, A. (eds.) SITIS 2006. LNCS, vol. 4879, pp. 247–258. Springer, Heidelberg (2009) Google Scholar
  184. 184.
    Eklund, P., Ducrou, J.: Navigation and annotation with formal concept analysis. In: Kang, B.-H., Richards, D. (eds.) PKAW 2008. LNCS, vol. 5465, pp. 118–121. Springer, Heidelberg (2009) Google Scholar
  185. 185.
    Cigarrán, J.M., Peñas, A., Gonzalo, J., Verdejo, M.F.: Automatic selection of noun phrases as document descriptors in an FCA-based information retrieval system. In: Ganter, B., Godin, R. (eds.) ICFCA 2005. LNCS (LNAI), vol. 3403, pp. 49–63. Springer, Heidelberg (2005) Google Scholar
  186. 186.
    Recio-García, J.A., Gómez-Martín, M.A., Díaz-Agudo, B., González-Calero, P.A.: Improving annotation in the semantic web and case authoring in textual CBR. In: Göker, M.H., Roth-Berghofer, T.R., Güvenir, H.A. (eds.) ECCBR 2006. LNCS (LNAI), vol. 4106, pp. 226–240. Springer, Heidelberg (2006) Google Scholar
  187. 187.
    Liu, M., Shao, M., Zhang, W., Wu, C.: Reduction method for concept lattices based on rough set theory and its application. Comput. Math. Appl. 53(9), 1390–1410 (2007)MathSciNetzbMATHGoogle Scholar
  188. 188.
    Lungley, D., Kruschwitz, U.: Automatically maintained domain knowledge: initial findings. In: Berrut, C., Soule-Dupuy, C., Mothe, J., Boughanem, M. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 739–743. Springer, Heidelberg (2009) Google Scholar
  189. 189.
    Ahmad, I., Jang, T.: Old fashion text-based image retrieval using FCA. ICIP 3, 33–36 (2003)Google Scholar
  190. 190.
    Ducrou, J., Vormbrock, B., Eklund, P.: FCA-based browsing and searching of a collection of images. In: Øhrstrøm, P., Hitzler, P., Schärfe, H. (eds.) ICCS 2006. LNCS (LNAI), vol. 4068, pp. 203–214. Springer, Heidelberg (2006) Google Scholar
  191. 191.
    Ducrou, J.: DVDSleuth: a case study in applied formal concept analysis for navigating web catalogs. In: Priss, U., Hill, R., Polovina, S. (eds.) ICCS 2007. LNCS (LNAI), vol. 4604, pp. 496–500. Springer, Heidelberg (2007) Google Scholar
  192. 192.
    Amato, G., Meghini, C.: Faceted content-based image retrieval. In: 19th International Workshop on Database and Expert Systems Applications (DEXA 2008), 1–5 September 2008, Turin, Italy, pp. 402–406 (2008)Google Scholar
  193. 193.
    Ferré, S.: Camelis: a logical information system to organise and browse a collection of documents. Int. J. Gen. Syst. 38(4), 379–403 (2009)zbMATHGoogle Scholar
  194. 194.
    Poelmans, J., Elzinga, P., Viaene, S., Dedene, G.: Formally analysing the concepts of domestic violence. Expert Syst. Appl. 38(4), 3116–3130 (2011)Google Scholar
  195. 195.
    Wolff, K.E.: States, transitions, and life tracks in temporal concept analysis. In: Formal Concept Analysis, Foundations and Applications, pp. 127–148 (2005)Google Scholar
  196. 196.
    Elzinga, P., Poelmans, J., Viaene, S., Dedene, G., Morsing, S.: Terrorist threat assessment with formal concept analysis. In: IEEE International Conference on Intelligence and Security Informatics, ISI 2010, Vancouver, BC, Canada, May 23–26, 2010, Proceedings, pp. 77–82 (2010)Google Scholar
  197. 197.
    Elzinga, P., Wolff, K.E., Poelmans, J.: Analyzing chat conversations of pedophiles with temporal relational semantic systems. In: 2012 European Intelligence and Security Informatics Conference, EISIC 2012, Odense, Denmark, August 22–24, 2012, pp. 242–249 (2012)Google Scholar
  198. 198.
    Bullens, R., Van Horn, J.: Daad uit liefde: Gedwongen prostitutie van jonge meisjes. Justitiele Verkenningen 26(6), 25–41 (2000)Google Scholar
  199. 199.
    Koester, B., Schmidt, S.: Information superiority via formal concept analysis. In: Argamon, S., Howard, N. (eds.) Computational Methods for Counterterrorism, pp. 143–171. Springer, Heidelberg (2009)Google Scholar
  200. 200.
    Obiedkov, S.A., Kourie, D.G., Eloff, J.H.P.: Building access control models with attribute exploration. Comput. Secur. 28(1–2), 2–7 (2009)Google Scholar
  201. 201.
    Dau, F., Knechtel, M.: Access policy design supported by FCA methods. [249] 141–154Google Scholar
  202. 202.
    Zhukov, L.E.: Spectral clustering of large advertiser datasets. Overture R&D, Technical Report (2004)Google Scholar
  203. 203.
    Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: ACM Conference on Electronic Commerce, pp. 158–167 (2000)Google Scholar
  204. 204.
    Besson, J., Robardet, C., Boulicaut, J.F., Rome, S.: Constraint-based bi-set mining for biologically relevant pattern discovery in microarray data. Intell. Data Anal. J. 9(1), 59–82 (2005)Google Scholar
  205. 205.
    Szathmary, L., Napoli, A.: CORON: A Framework for Levelwise Itemset Mining Algorithms. In: Supplements Proceedings of ICFCA 2005, Lens, France, pp. 110–113, February 2005Google Scholar
  206. 206.
    Szathmary, L., Napoli, A., Kuznetsov, S.O.: ZART: a multifunctional itemset mining algorithm. In: Proceedings of the 5th International Conference on Concept Lattices and Their Applications (CLA 2007), pp. 26–37. Montpellier, France, October 2007Google Scholar
  207. 207.
    Crystal, D.: A dictionary of linguistics and phonetics, 3rd edn. Blackwell Publishers, Oxford (1991)Google Scholar
  208. 208.
    Symeonidis, P., Ruxanda, M.M., Nanopoulos, A., Manolopoulos, Y.: Ternary semantic analysis of social tags for personalized music recommendation. In: Bello, J.P., Chew, E., Turnbull, D. (eds.): ISMIR, pp. 219–224 (2008)Google Scholar
  209. 209.
    Alqadah, F., Reddy, C., Hu, J., Alqadah, H.: Biclustering neighborhood-based collaborative filtering method for top-n recommender systems. Knowl. Inf. Syst. 44, 1–17 (2014)Google Scholar
  210. 210.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)Google Scholar
  211. 211.
    Ignatov, D.I., Poelmans, J., Dedene, G., Viaene, S.: A new cross-validation technique to evaluate quality of recommender systems. In: Kundu, M., Mitra, S., Mazumdar, D., Pal, S. (eds.) Perception and Machine Intelligence. LNCS, vol. 7143, pp. 195–202. Springer, Heidelberg (2012)Google Scholar
  212. 212.
    Brin, S., Davis, J., García-Molina, H.: Copy detection mechanisms for digital documents. SIGMOD Rec. 24(2), 398–409 (1995)Google Scholar
  213. 213.
    Broder, A.Z., Glassman, S.C., Manasse, M.S., Zweig, G.: Syntactic clustering of the web. Comput. Netw. 29(8–13), 1157–1166 (1997)Google Scholar
  214. 214.
    Ilyinsky, S., Kuzmin, M., Melkov, A., Segalovich, I.: An efficient method to detect duplicates of web documents with the use of inverted index. In: Proceedings of the 11th International World Wide Web Conference (WWW 2002), Honolulu, Hawaii, USA, 7–11 May 2002, ACM (2002)Google Scholar
  215. 215.
    Broder, A.Z., Charikar, M., Frieze, A.M., Mitzenmacher, M.: Min-wise independent permutations (extended abstract). In: Proceedings of the Thirtieth Annual ACM Symposium on the Theory of Computing, Dallas, Texas, USA, May 23–26, 1998, pp. 327–336 (1998)Google Scholar
  216. 216.
    Broder, A.: Identifying and filtering near-duplicate documents. In: Giancarlo, R., Sankoff, D. (eds.) CPM 2000. LNCS, vol. 1848, pp. 1–10. Springer, Heidelberg (2000) Google Scholar
  217. 217.
    Grahne, G., Zhu, J.: Efficiently using prefix-trees in mining frequent itemsets. In: FIMI 2003, Frequent Itemset Mining Implementations, Proceedings of the ICDM 2003 Workshop on Frequent Itemset Mining Implementations, 19 December 2003, Melbourne, Florida, USA (2003)Google Scholar
  218. 218.
    Karypis, G.: Cluto. a clustering toolkit. Technical Report: 2–017 MN 55455, University of Minnesota, Department of Computer Science Minneapolis, November 28 2003Google Scholar
  219. 219.
    Potthast, M., Stein, B.: New issues in near-duplicate detection. In: Data Analysis, Machine Learning and Applications - Proceedings of the 31st Annual Conference of the Gesellschaft für Klassifikation e.V., Albert-Ludwigs-Universität Freiburg, March 7–9, 2007, pp. 601–609 (2007)Google Scholar
  220. 220.
    Zelenkov, Y.G., Segalovich, I.V.: Comparative analysis of near-duplicate detection methods of web documents. In: Proceedings of the 9th All-Russian Scientific Conference Digital Libraries: Advanced Methods and Technologies, Digital Collections, Pereslavl-Zalessky, pp. 166–174 (2007) (in Russian)Google Scholar
  221. 221.
    Ignatov, D.I., Jánosi-Rancz, K.T., Kuznetzov, S.O.: Towards a framework for near-duplicate detection in document collections based on closed sets of attributes. Acta Univ. Sapientiae Inf. 1(2), 215–233 (2009)zbMATHGoogle Scholar
  222. 222.
    Ignatov, D., Kuznetsov, S., Lopatnikova, V., Selitskiy, I.: Development and aprobation of near duplicate detection system for collections of r&d documents. Bus. Inf. 4, 21–28 (2008). (in Russian)Google Scholar
  223. 223.
    Ley, M.: DBLP - some lessons learned. PVLDB 2(2), 1493–1500 (2009)Google Scholar
  224. 224.
    Benz, D., Hotho, A., Jäschke, R., Krause, B., Stumme, G.: Query logs as folksonomies. Datenbank-Spektrum 10(1), 15–24 (2010)Google Scholar
  225. 225.
    Doerfel, S., Jäschke, R.: An analysis of tag-recommender evaluation procedures. In: Seventh ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China, October 12–16, 2013, pp. 343–346 (2013)Google Scholar
  226. 226.
    Kuznetsov, S.O., Ignatov, D.I.: Concept stability for constructing taxonomies of web-site users. In: Obiedkov, S., Roth, C. (eds.), Proceedings of the Social Network Analysis and Conceptual Structures: Exploring Opportunities, Clermont-Ferrand (France), February 16, 2007, pp. 19–24 (2007)Google Scholar
  227. 227.
    Kuznetsov, S.: Stability as an estimate of the degree of substantiation of hypotheses derived on the basis of operational similarity. Nauchn. Tekh. Inf. Ser. 2(12), 21–29 (1990). (Automat. Document. Math. Linguist.)Google Scholar
  228. 228.
    Kuznetsov, S.O.: On stability of a formal concept. Ann. Math. Artif. Intell. 49(1–4), 101–115 (2007)MathSciNetzbMATHGoogle Scholar
  229. 229.
    Roth, C., Cointet, J.P.: Social and semantic coevolution in knowledge networks. Soc. Netw. 32, 16–29 (2010)Google Scholar
  230. 230.
    Yavorsky, R.: Research challenges of dynamic socio-semantic networks. In: Ignatov, D., Poelmans, J., Kuznetsov, S. (eds.): CEUR Workshop proceedings vol. 757, CDUD 2011 - Concept Discovery in Unstructured Data, pp. 119–122 (2011)Google Scholar
  231. 231.
    Howe, J.: The rise of crowdsourcing. Wired, San Francisco (2006)Google Scholar
  232. 232.
    Ignatov, D.I., Mikhailova, M., Kaminskaya, A.Y.Z., Malioukov, A.: Recommendation of ideas and antagonists for crowdsourcing platform witology. In: Proceedings of 8th RuSSIR, Springer (2014) (this volume)Google Scholar
  233. 233.
    Ignatov, D.I., Kaminskaya, A.Y., Konstantinova, N., Malyukov, A., Poelmans, J.: FCA-based recommender models and data analysis for crowdsourcing platform witology. In: Hernandez, N., Jäschke, R., Croitoru, M. (eds.) ICCS 2014. LNCS, vol. 8577, pp. 287–292. Springer, Heidelberg (2014) Google Scholar
  234. 234.
    Ignatov, D.I., Kaminskaya, A.Y., Konstantinova, N., Konstantinov, A.V.: Recommender system for crowdsourcing platform witology. In: 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), Warsaw, Poland, August 11–14, 2014, vol. II, pp. 327–335 (2014)Google Scholar
  235. 235.
    Ganter, B.: Attribute exploration with background knowledge. Theor. Comput. Sci. 217(2), 215–233 (1999). ORDAL’96MathSciNetzbMATHGoogle Scholar
  236. 236.
    Stumme, G., Maedche, A.: Fca-merge: Bottom-up merging of ontologies. In: Nebel, B. (ed.): IJCAI, Morgan Kaufmann, pp. 225–234 (2001)Google Scholar
  237. 237.
    Revenko, A., Kuznetsov, S.O.: Attribute exploration of properties of functions on sets. Fundam. Inform. 115(4), 377–394 (2012)MathSciNetzbMATHGoogle Scholar
  238. 238.
    Sertkaya, B.: A survey on how description logic ontologies benefit from FCA. In: Proceedings of the 7th International Conference on Concept Lattices and Their Applications, Sevilla, Spain, October 19–21, 2010, pp. 2–21 (2010)Google Scholar
  239. 239.
    Sertkaya, B.: Ontocomp: A protégé plugin for completing OWL ontologies. In: The Semantic Web: Research and Applications, 6th European Semantic Web Conference, ESWC 2009, Heraklion, Crete, Greece, May 31-June 4, 2009, Proceedings, pp. 898–902 (2009)Google Scholar
  240. 240.
    Baader, F., Ganter, B., Sertkaya, B., Sattler, U.: Completing description logic knowledge bases using formal concept analysis. In: IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, January 6–12, 2007, pp. 230–235 (2007)Google Scholar
  241. 241.
    Rudolph, S.: Relational exploration: combining description logics and formal concept analysis for knowledge specification. Ph.D. thesis, Dresden University of Technology (2006)Google Scholar
  242. 242.
    Potoniec, J., Rudolph, S., Lawrynowicz, A.: Towards combining machine learning with attribute exploration for ontology refinement. In: Proceedings of the ISWC 2014 Posters & Demonstrations Track a track within the 13th International Semantic Web Conference, ISWC 2014, Riva del Garda, Italy, October 21, 2014, pp. 229–232 (2014)Google Scholar
  243. 243.
    Jäschke, R., Rudolph, S.: Attribute exploration on the web. In: Cellier, P., Distel, F., Ganter, B. (eds.): Contributions to the 11th International Conference on Formal Concept Analysis, Technische Universit Dresden, pp. 19–34, May 2013Google Scholar
  244. 244.
    Codocedo, V., Lykourentzou, I., Napoli, A.: A semantic approach to concept lattice-based information retrieval. Ann. Math. Artif. Intell. 72(1–2), 169–195 (2014)MathSciNetzbMATHGoogle Scholar
  245. 245.
    Tilley, T., Cole, R., Becker, P., Eklund, P.: A survey of formal concept analysis support for software engineering activities. In: Wille, R., Stumme, G., Ganter, B. (eds.) Formal Concept Analysis. LNCS (LNAI), vol. 3626, pp. 250–271. Springer, Heidelberg (2005) zbMATHGoogle Scholar
  246. 246.
    Arévalo, G., Desnos, N., Huchard, M., Urtado, C., Vauttier, S.: Formal concept analysis-based service classification to dynamically build efficient software component directories. Int. J. Gen. Syst. 38(4), 427–453 (2009)zbMATHGoogle Scholar
  247. 247.
    Mirkin, B.G., Kuznetsov, S.O., Slkezak, D., Hepting, D.H. (eds.): RSFDGrC 2011. LNCS, vol. 6743. Springer, Heidelberg (2011)Google Scholar
  248. 248.
    Eklund, P., Ducrou, J., Brawn, P.: Concept lattices for information visualization: can novices read line-diagrams? In: Eklund, P. (ed.) ICFCA 2004. LNCS (LNAI), vol. 2961, pp. 57–73. Springer, Heidelberg (2004) Google Scholar
  249. 249.
    Dau, F., Rudolph, S., Kuznetsov, S.O. (eds.): ICCS 2009. LNCS, vol. 5662. Springer, Heidelberg (2009) zbMATHGoogle Scholar
  250. 250.
    Ojeda-Aciego, M., Outrata, J. (eds.): Proceedings of the tenth international conference on concept lattices and their applications. La Rochelle, France, October 15–18, 2013. CLA. vol. 1062 of CEUR Workshop Proceedings, (2013)Google Scholar

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Authors and Affiliations

  1. 1.National Research University Higher School of EconomicsMoscowRussia

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