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Feature Selection by Principle Component Analysis for Mining Frequent Association Rules

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Proceedings of the First International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’16)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 450))

Abstract

Data mining techniques have been increasingly studied. Extracting the association rules have been the focus of this studies. Recently research have focused on association rules to help uncover relationships between seemingly unrelated data in a relational database or other information repository. The large size of data makes the extraction of association rules hard task. In this paper, we propose a new method for dimension reduction and feature selection based on the Principal Component Analysis, then find the association rules by using the FP-Growth Algorithm. Experimental results reveals that the reduction technique can discover the same rules obtained by the original data.

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References

  1. Abdulla, H.D., Abdelrahman, A.S., Snášel, V., Aldosari, H.: Using singular value decomposition as a solution for search result clustering. In: Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014, pp. 53–61. Springer (2014)

    Google Scholar 

  2. Aggarwal, C.C., Bhuiyan, M., Al Hasan, M.: Frequent pattern mining algorithms: a survey. In: Frequent Pattern Mining, pp. 19–64 (2014)

    Google Scholar 

  3. Aggarwal, C.C., Han, J. (eds.): Frequent Pattern Mining. Springer (2014)

    Google Scholar 

  4. Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. SIGMOD Rec. 22(2), 207–216 (1993)

    Article  Google Scholar 

  5. Amir, A., Feldman, R., Kashi, R.: A new and versatile method for association generation. Inf. Syst. 22(6/7), 333–347 (1997)

    Article  MATH  Google Scholar 

  6. Aswani Kumar, Ch., Srinivas, S.: Mining associations in health care data using formal concept analysis and singular value decomposition. J. Biol. Syst. 18(04), 787–807 (2010)

    Google Scholar 

  7. Aswani Kumar, Ch.: Mining association rules using non-negative matrix factorization and formal concept analysis. In: Venugopal, K.R., Patnaik, L.M. (eds.) Computer Networks and Intelligent Computing. Communications in Computer and Information Science, vol. 157, pp. 31–39. Springer, Berlin (2011)

    Chapter  Google Scholar 

  8. Chaves, R., Ramrez, J., Grriz, J.M.: Integrating discretization and association rule-based classification for alzheimers disease diagnosis. Expert Syst. Appl. 40(5), 1571–1578 (2013)

    Article  Google Scholar 

  9. Chaves, R., Ramírez, J., Górriz, J.M., Puntonet, C.G., et al.: Alzheimers disease, initiative, neuroimaging, association rule-based feature selection method for alzheimers disease diagnosis. Expert Syst. Appl. 39(14), 11766–11774 (2012)

    Google Scholar 

  10. Chen, Y., Li, F., Fan, J.: Mining association rules in big data with NGEP. Cluster Comput. 18(2), 577–585 (2015)

    Article  Google Scholar 

  11. Das, S., Nath, B.: Dimesionality reduction using association rule mining. In: IEEE Region 10 and the Third International Conference on Industrial and Information Systems. ICIIS 2008, pp. 1–6, Dec 2008

    Google Scholar 

  12. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)

    MATH  Google Scholar 

  13. Hahsler, M., Hornik, K., Reutterer, T.: Implications of probabilistic data modeling for mining association rules. In: Spiliopoulou, M., Kruse, R., Borgelt, C., Nrnberger, A., Gaul, W. (eds.) From Data and Information Analysis to Knowledge Engineering. Studies in Classification, Data Analysis, and Knowledge Organization, pp. 598–605. Springer, Berlin (2006)

    Chapter  Google Scholar 

  14. Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. SIGMOD Rec. 29(2), 1–12 (2000)

    Article  Google Scholar 

  15. Inan, O., Uzer, M.S., Yılmaz, N.: A new hybrid feature selection method based on association rules and PCA for detection of breast cancer. Int. J. Innov. Comput. Inf. Control 9(2), 727–729 (2013)

    Google Scholar 

  16. Kim, K.T., Seol, W.S., Kim, U.M., Youn, H.Y.: Latent semantic analysis for mining rules in big data environment. In: 2014 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), pp. 193–200. IEEE (2014)

    Google Scholar 

  17. Molina, L.C., Belanche, L., Nebot, À.: Feature selection algorithms: a survey and experimental evaluation. In: Proceedings of the 2002 IEEE International Conference on Data Mining, ICDM ’02, pp. 306–, Washington, DC, USA. IEEE Computer Society (2002)

    Google Scholar 

  18. Oweis, N.E., Owais, S.S., George, W., Suliman, M.G., Snášel, V.: A survey on big data, mining: (tools, techniques, applications and notable uses). In: Intelligent Data Analysis and Applications, pp. 109–119. Springer (2015)

    Google Scholar 

  19. Piatetsky-Shapiro, G.: Discovery, analysis and presentation of strong rules. In: Piatetsky-Shapiro, G., Frawley, W.J. (eds.) Knowledge Discovery in Databases, pp. 229–248. AAAI Press (1991)

    Google Scholar 

  20. Powell, W.B.: Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics). Wiley-Interscience (2007)

    Google Scholar 

  21. Soysal, Ö.M.: Association rule mining with mostly associated sequential patterns. Expert Syst. Appl. 42(5), 2582–2592 (2015)

    Google Scholar 

  22. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Incremental singular value decomposition algorithms for highly scalable recommender systems. In: Proceedings of the 5th International Conference in Computers and Information Technology (2002)

    Google Scholar 

  23. Snasel, V., Abdulla, H.M.D., Polovincak, M.: Behavior of the concept lattice reduction to visualizing data after using matrix decompositions. In: 4th International Conference on Innovations in Information Technology. IIT ’07, pp. 392–396, Nov 2007

    Google Scholar 

  24. Snasel, V., Gajdos, P., Dahwa Abdulla, H.M., Polovincak, M.: Using matrix decompositions in formal concept analysis. In: The 10th Intenational Connference of Information Systems Implementations and Modeling (2007)

    Google Scholar 

  25. Snasel, V., Polovincak, M., Dahwa Abdulla, H.M., Horak, Z.: On concept lattices and implication bases from reduced contexts. In: Eklund, P.W., Haemmerl, O. (eds.) ICCS Supplement, CEUR Workshop Proceedings, vol. 354, pp. 83–90. CEUR-WS.org (2008)

    Google Scholar 

  26. Snasel, V., Polovincak, M., Dahwa Abdulla, H.M., Horak, Z.: On knowledge structures reduction. In: Snsel, V., Abraham, A., Saeed, K., Pokorn, J. (eds.) CISIM, pp. 33–37. IEEE Computer Society (2008)

    Google Scholar 

  27. Snchez, D., Vila, M.A., Cerda, L., Serrano, J.M.: Association rules applied to credit card fraud detection. Expert Syst. Appl. 36(2, Part 2), 3630–3640 (2009)

    Google Scholar 

  28. Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston (2005)

    Google Scholar 

  29. Thomasian, A.: Singular value decomposition, clustering, and indexing for similarity search for large data sets in high-dimensional spaces (2015)

    Google Scholar 

  30. Yunyan, L., Juan, C.: Application of association rules mining in marketing decision-making based on rough set. In: 2010 International Conference on E-Business and E-Government (ICEE), pp. 3749–3752, May 2010

    Google Scholar 

  31. Zhou, X., He, J., Huang, G., Zhang, Y.: SVD-based incremental approaches for recommender systems. J. Comput. Syst. Sci. 81(4), 717–733 (2015)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgments

This work is partially supported by Grant of SGS No. SP2016/97, VB—Technical University of Ostrava, Czech Republic.

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Authors and Affiliations

  1. Department of Computer Sciences, Sudan University for Sciences and Technology, Khartoum, Sudan

    Tayseer M. F. Taha & Eltayeb Shomo

  2. Faculty of Electrical Engineering and Computer Science, Department of Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Tayseer M. F. Taha, Eltayeb Shomo, Nour E. Oweis & Vaclav Snasel

Authors
  1. Tayseer M. F. Taha
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  2. Eltayeb Shomo
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  3. Nour E. Oweis
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  4. Vaclav Snasel
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Corresponding author

Correspondence to Nour E. Oweis .

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Editors and Affiliations

  1. Sci N/w for Innova and Research Exc, Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  2. Rostov State Transport University, Rostov-on-Don, Russia

    Sergey Kovalev

  3. Bauman Moscow State Technical University, Moscow, Russia

    Valery Tarassov

  4. Institute of Mathematics, VŠB-Technical University of Ostrava, Ostrava, Czech Republic

    Václav Snášel

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Taha, T.M.F., Shomo, E., Oweis, N.E., Snasel, V. (2016). Feature Selection by Principle Component Analysis for Mining Frequent Association Rules. In: Abraham, A., Kovalev, S., Tarassov, V., Snášel, V. (eds) Proceedings of the First International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’16). Advances in Intelligent Systems and Computing, vol 450. Springer, Cham. https://doi.org/10.1007/978-3-319-33609-1_9

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  • DOI: https://doi.org/10.1007/978-3-319-33609-1_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33608-4

  • Online ISBN: 978-3-319-33609-1

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