A Study on Quantitative Association Rules Mining Algorithm Based on Clustering Algorithm( COMPUTATIONAL INTELLIGENCE)

In order to develop a data mining system for huge database mainly composed of numerical attributes, there exists necessary process to decide valid quantization of the numerical attributes. Though the clustering algorithm can provide useful information for the quantization problem, it is difficult to formulate appropriate clusters for rule extraction in terms of appropriate dimension, cluster size, and shape. In this paper, we propose a new method of quantitative association rules extraction that can quantize the attribute by applying clustering algorithm and extract rules simultaneously. From the results of numerical experiments using benchmark data, the method is found to be effective for actual applications.

[1]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[2]  T Purusothaman,et al.  UTILITY SENTIENT FREQUENT ITEM SET MINING AND ASSOCIATION RULE MINING: A LITERATURE SURVEY AND COMPARATIVE STUDY , 2009 .

[3]  Miguel Delgado,et al.  Mining Fuzzy Association Rules: An Overview , 2005 .

[4]  Toshihiko Watanabe,et al.  Mining fuzzy association rules of specified output field , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[5]  Vikram Pudi,et al.  Fuzzy association rule mining algorithm for fast and efficient performance on very large datasets , 2009, 2009 IEEE International Conference on Fuzzy Systems.

[6]  Ramakrishnan Srikant,et al.  Mining quantitative association rules in large relational tables , 1996, SIGMOD '96.

[7]  Charu C. Aggarwal,et al.  An Introduction to Cluster Analysis , 2018, Data Clustering: Algorithms and Applications.

[8]  Martine De Cock,et al.  Fuzzy versus quantitative association rules: a fair data-driven comparison , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[9]  D. F. Andrews,et al.  Data : a collection of problems from many fields for the student and research worker , 1985 .

[10]  Toshihiko Watanabe,et al.  Fuzzy rule extraction based on the mining generalized association rules , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[11]  Ming-Syan Chen,et al.  Reducing Redundancy in Subspace Clustering , 2009, IEEE Transactions on Knowledge and Data Engineering.

[12]  Hisao Ishibuchi,et al.  Fuzzy Rule Selection By Data Mining Criteria And Genetic Algorithms , 2002, GECCO.

[13]  Guoqing Chen,et al.  Fuzzy association rules and the extended mining algorithms , 2002, Inf. Sci..

[14]  Man Lung Yiu,et al.  Iterative projected clustering by subspace mining , 2005, IEEE Transactions on Knowledge and Data Engineering.

[15]  Yi-Chung Hu,et al.  Discovering fuzzy association rules using fuzzy partition methods , 2003, Knowl. Based Syst..

[16]  Eyke Hüllermeier,et al.  In Defense of Fuzzy Association Analysis , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[17]  E. K. Harris,et al.  Multivariate Interpretation of Clinical Laboratory Data. , 1989 .

[18]  Ramakrishnan Srikant,et al.  Mining generalized association rules , 1995, Future Gener. Comput. Syst..

[19]  Dimitrios Gunopulos,et al.  Automatic subspace clustering of high dimensional data for data mining applications , 1998, SIGMOD '98.

[20]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.