Abstract
One of the approaches applied in data analysis is related to the theory of concept lattices, also known as Formal Concept Analysis (FCA), which is suitable for processing and analysis of object-attribute input data models. Concept lattice represents hierarchically organized structure of clusters of objects (concepts) based on the presence of their shared attributes. While basic FCA framework works only with binary input data tables, several approaches were introduced in order to process fuzzy attributes. The model of Generalized One-Sided Concept Lattices (GOSCL) is suitable to work with different types of attributes used in input data tables, which helped in understanding and interpretation of analysis. One of the main issues which remains is large number of concepts for visualization to user. The solution is to provide user with the reduction methods and advanced dynamic visualization of concept lattices and their reductions. In this paper we introduce and compare some of the implemented visualizations and reductions applied to concept lattices generated from input data.
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References
Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer Verlag, Berlin (1999)
Krajci, S.: A generalized concept lattice. Logic Journal of IGPL 13(5), 543–550 (2005)
Medina, J., Ojeda-Aciego, M., Ruiz-Calvino, J.: Formal concept analysis via multi-adjoint concept lattices. Fuzzy Set. Syst. 160, 130–144 (2009)
Pocs, J.: Note on generating fuzzy concept lattices via Galois connections. Inform. Sci. 185(1), 128–136 (2012)
Antoni, L., Krajci, S., Kridlo, O., Macek, B., Piskova, L.: On heterogeneous formal contexts. Fuzzy Set. Syst. 234, 22–33 (2014)
Krajci, S.: Cluster based efficient generation of fuzzy concepts. Neural Netw. World 13(5), 521–530 (2003)
Butka, P., Pocs, J.: Generalization of one-sided concept lattices. Comput. Inf. 32(2), 355–370 (2013)
Butka, P., Pocs, J., Pocsova, J.: Use of concept lattices for data tables with different types of attributes. J. Inf. Organ. Sci. 36(1), 1–12 (2012)
Butka, P., Pocs, J., Pocsova, J.: On equivalence of conceptual scaling and generalized one-sided concept lattices. Inform. Sci. 259, 57–70 (2014)
Butka, P., Pocsova, J., Pocs, J.: Design and implementation of incremental algorithm for creation of generalized one-sided concept lattices. In: Proceedings of CINTI 2012, Budapest, Hungary, pp. 373–378 (2011)
Butka, P., Pocs, J., Pocsová, J.: Reduction of concepts from generalized one-sided concept lattice based on subsets quality measure. Adv. Intell. Syst. Comput. 314, 101–111 (2015)
Antoni, L., Krajci, S., Kridlo, O.: Randomized fuzzy formal contexts and relevance of one-sided concepts. LNAI (Subseries of LNCS) 9113, 183–199 (2014)
Melo, C., Le-Grand, B., Aufaure, A.: Browsing large concept lattices through tree extraction and reduction methods. Int. J. Intell. Inf. Technol. (IJIIT) 9(4), 16–34 (2013)
Gajdos, P., Moravec, P., Snasel, V.: Concept lattice generation by singular value decomposition. In: Proceedings of CLA 2004, pp. 13–22 (2004)
Snasel, V., Polovincak, M., Abdulla, H.: Concept lattice reduction by singular value decomposition. In: Proceedings of the SYRCoDIS 2007, Moscow, Russia (2007)
Kumar, ChA, Srinivas, S.: Concept lattice reduction using fuzzy K-Means clustering. Expert Syst. Appl. 37(3), 2696–2704 (2010)
Dias, S., Vieira, N.: Reducing the size of concept lattices: the JBOS approach. In: Proceedings of CLA 2010, pp. 80–91 (2010)
Quan, T., Hui, S., Cao, T.: A fuzzy FCA-based approach to conceptual clustering for automatic generation of concept hierarchy on uncertainty data. In: Proceedings of CLA 2004, pp. 1–12 (2004)
Lengler, R., Eppler, M.: Towards a periodic table of visualization methods for management. In: Proceedings of the International Conference on Graphic and Visualization in Engineering (GVE 2007), Clearwater, Florida, pp. 83–88 (2007)
Wills, G.: Visualizing hierarchical data. In: Encyclopedia of Database Systems, pp. 3425–3432 (2009)
Theron, R.: Hierarchical-temporal data visualization using a tree-ring metaphor. In: Smart Graphics. Springer, Berlin, pp. 70–81 (2006)
Itoh, T., Yamaguchi, Y., Ikehata, Y., Kajinaga, Y.: Hierarchical data visualization using a fast rectangle-packing algorithm. IEEE Trans. Visual Comput. Graphics 10(3), 302–313 (2004)
Neumann, P., Schlechtweg, S., Carpendale, S.: ArcTrees: Visualizing relations in hierarchical data. In: Proceedings of EuroVis 2005, pp. 53–60 (2005)
Jadeja, M., Shah, K.: Tree-map: A visualization tool for large data. In: Proceedings of 1st International Workshop on Graph Search and Beyond (GSB 2015), pp. 9–13 (2015)
Gotz, D.: Dynamic Voronoi Treemaps: a visualization technique for time-varying hierarchical data. IBM Research Technical Report, RC25132 (2011)
Crampes, M., Oliveira-Kumar, J., Ranwez, S., Villerd, J.: Visualizing social photos on a hasse diagram for eliciting relations and indexing new photos. IEEE Trans. Visual Comput. Graphics 15(6), 985–992 (2009)
Fattore, M., Arcagni, A., Barberis, S.: Visualizing partially ordered sets for socioeconomic analysis. Revista Colombiana de Estadística 37(2), 437–450 (2014)
Holten, D.: Hierarchical edge bundles: visualization of adjacency relations in hierarchical data. IEEE Trans. Visual Comput. Graphics 12(5), 741–748 (2006)
Acknowledgments
The work presented in this paper was supported by the Slovak VEGA grant 1/0493/16 and Slovak KEGA grant 025TUKE-4/2015.
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Smatana, M., Butka, P. (2017). Dynamic Visualization of Generalized One-Sided Concept Lattices and Their Reductions. In: Borzemski, L., Grzech, A., Świątek, J., Wilimowska, Z. (eds) Information Systems Architecture and Technology: Proceedings of 37th International Conference on Information Systems Architecture and Technology – ISAT 2016 – Part I. Advances in Intelligent Systems and Computing, vol 521. Springer, Cham. https://doi.org/10.1007/978-3-319-46583-8_5
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