Abstract
This paper proposes a new algorithm for classification based on association rule with interestingness measures. The proposed algorithm uses a tree structure for maintenance of related information in each node, thus making the process of generating rules fast. Besides, the proposed algorithm can be easily extended to integrate some measures together for ranking rules. Experiments are also made to show the efficiency of the proposed approach for different settings. The mining time for different interestingness measures is varied only a little when ten measures are integrated.
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References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Very Large Data Bases, VLDB 1994, pp. 487–499 (1994)
Aljandal, W., Hsu, W.H., Bahirwani, V., Caragea, D., Weninger, T.: Validation-based normalization and selection of interestingness measures for association rules. In: The 18th International Conference on Artificial Neural Networks in Engineering, pp. 1–8 (2008)
Hilderman, R., Hamilton, H.: Knowledge discovery and measures of interest. Kluwer Academic Publishers (2001)
Huynh, X.-H., Guillet, F., Blanchard, J., Kuntz, P., Briand, H., Gras, R.: A Graph-Based Clustering Approach to Evaluate Interestingness Measures: A Tool and a Comparative Study. In: Guillet, F.J., Hamilton, H.J. (eds.) Quality Measures in Data Mining. SCI, vol. 43, pp. 25–50. Springer, Heidelberg (2007)
Lee, Y.K., Kim, W.Y., Cai, Y., Han, J.: CoMine: efficient mining of correlated patterns. In: IEEE International Conference on Data Mining, pp. 581–584 (2003)
Lenca, P., Meyer, P., Vaillant, P., Lallich, S.: On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid. European Journal of Operational Research 184(2), 610–626 (2008)
Li, W., Han, J., Pei, J.: CMAR: Accurate and efficient classification based on multiple class-association rules. In: The 1st IEEE International Conference on Data Mining, San Jose, California, USA, pp. 369–376 (2001)
Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: The 4th International Conference on Knowledge Discovery and Data Mining, New York, USA, pp. 80–86 (1998)
Liu, Y.Z., Jiang, Y.C., Liu, X., Yang, S.L.: CSMC: A combination strategy for multiclass classification based on multiple association rules. Knowledge-Based Systems 21(8), 786–793 (2008)
Nguyen, L.T.T., Vo, B., Hong, T.P., Thanh, H.C.: Classification based on association rules: A lattice-based approach. Expert Systems with Applications 39(13), 11357–11366 (2012)
Omiecinski, E.: Alternative interest measures for mining associations in databases. IEEE Transaction on Knowledge and Data Engineering 15(1), 57–69 (2003)
Piatetsky-Shapiro, G.: Discovery, analysis, and presentation of strong rules. In: Knowledge Discovery in Databases, pp. 229–248 (1991)
Piatetsky-Shapiro, G., Steingold, S.: Measuring lift quality in database marketing. SIGKDD Explorations 2(2), 76–80 (2000)
Quinlan, J.R.: C4.5: program for machine learning. Morgan Kaufmann (1992)
Tan, P.N., Kumar, V., Srivastava, J.: Selecting the right objective measure for association analysis. Information Systems 29(4), 293–313 (2004)
Thabtah, F., Cowling, P., Peng, Y.: MMAC: A new multi-class, multi-label associative classification approach. In: The 4th IEEE International Conference on Data Mining, Brighton, UK, pp. 217–224 (2004)
Thonangi, R., Pudi, V.: ACME: An Associative Classifier Based on Maximum Entropy Principle. In: Jain, S., Simon, H.U., Tomita, E. (eds.) ALT 2005. LNCS (LNAI), vol. 3734, pp. 122–134. Springer, Heidelberg (2005)
Tolun, M.R., Abu-Soud, S.M.: ILA: An inductive learning algorithm for production rule discovery. Expert Systems with Applications 14(3), 361–370 (1998)
Tolun, M.R., Sever, H., Uludag, M., Abu-Soud, S.M.: ILA-2: An inductive learning algorithm for knowledge discovery. Cybernetics and Systems 30(7), 609–628 (1999)
Veloso, A., Meira Jr., W., Zaki, M.J.: Lazy associative classification. In: IEEE International Conference on Data Mining, ICDM 2006, Hong Kong, China, pp. 645–654 (2006)
Veloso, A., Meira Jr., W., Goncalves, M., Almeida, H.M., Zaki, M.J.: Calibrated lazy associative classification. Information Sciences 181(13), 2656–2670 (2011)
Vo, B., Le, B.: A Novel Classification Algorithm Based on Association Rules Mining. In: Richards, D., Kang, B.-H. (eds.) PKAW 2008. LNCS (LNAI), vol. 5465, pp. 61–75. Springer, Heidelberg (2009)
Vo, B., Le, B.: Interestingness measures for association rules: Combination between lattice and hash tables. Expert Systems with Applications 38(9), 11630–11640 (2011)
Yin, X., Han, J.: CPAR: Classification based on predictive association rules. In: SIAM International Conference on Data Mining, SDM 2003, San Francisco, CA, USA, pp. 331–335 (2003)
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Nguyen, L.T.T., Vo, B., Hong, TP., Thanh, H.C. (2012). Interestingness Measures for Classification Based on Association Rules. In: Nguyen, NT., Hoang, K., Jȩdrzejowicz, P. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2012. Lecture Notes in Computer Science(), vol 7654. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34707-8_39
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DOI: https://doi.org/10.1007/978-3-642-34707-8_39
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