Mining association rules through integration of clustering analysis and ant colony system for health insurance database in Taiwan

Expert Systems with Applications

Volumn 33, Issue 3, 2007, Pages 794-808

  Kuo, R J ^a   Lin, S Y ^a   Shih, C W ^b  

^a NATIONAL TAIPEI UNIVERSITY OF TECHNOLOGY   (Taiwan)

^b NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

Author keywords

Ant colony system; Association rule; Cluster; Data mining

Indexed keywords

ASSOCIATION RULES; DATA MINING; DATABASE SYSTEMS; HEALTH CARE; INSURANCE; LEARNING SYSTEMS;

ANT COLONY SYSTEM; NATIONAL HEALTH INSURANCE BUREAU; SYSTEM BASED CLUSTERING ALGORITHM (ASCA);

GLOBAL OPTIMIZATION;

EID: 33847628818     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2006.08.035     Document Type: Article

References (32)

1. Agrawal R., Imielinski T., and Swami A. Database mining: a performance perspective. IEEE Transactions on Knowledge and Data Engineering 5 6 (1993) 914-925 (Special issue on Learning and Discovery in Knowledge-Based Databases)
2. Agrawal, R., Imielinski, T., & Swami, A. (1993b). Mining association rules between sets of items in large databases. In Proc. ACM-SIGMOD int. conf. management of data (SIGMOD'93), May, Washington, USA (pp. 207-216).
3. Agrawal R., and Shafer J.C. Parallel mining of association rules. IEEE Transactions on Knowledge and Data Engineering 8 6 (1996) 962-969
4. Bayardo, R. J. (1998). Efficiently mining long patterns from database. In Proceedings of the ACM SIGMOD international conference on management of data, Washington, USA (pp. 85-93).
5. Bellaachia, A., Portnoy, D., Chen, Y., & Elkahoun, A. G. (2002). E-CAST: a data mining algorithm for gene expression data. In 2nd workshop on data mining in bioinformatics, July (pp. 49-54).
6. Berkhin, P. (2002). Survey of clustering data mining techniques. Accrue Software, Inc. Available from http://www.accrue.com/products/researchpapers.html.
7. Carpenter G.A., and Grossberg S. ART2: self-organization of stable category recognition codes for analog input pattern. Applied Optics 26 (1987) 4919-4930
8. Dorigo M., and Gambardella L.M. Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1 (1997) 53-66
9. Fayyad, U. (1997). Data mining and knowledge discovery in databases: implications for scientific databases. In Scientific and statistical database management, 1997 proceedings, ninth international conference (pp. 2-11).
10. Fayyad U., Piatetsky-Shapiro G., and Smyth P. From data mining to knowledge discovery in database. American Association for Artificial Intelligence August (1996) 37-54
11. Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. In: Proceedings of the ACM SIGMOD international conference on management of data, Dallas, TX, USA (pp.1-12).
12. Kohonen T. Self-organizing maps: optimization approaches. In: Kohonen T., Makisara K., Simula O., and Kangas J. (Eds). Artificial neural networks (1991), Elsevier, Amsterdam, The Netherlands 981-990
13. Koyuturk M., Grama A., and Ramakrishnan N. Member, compression, clustering, and pattern discovery in very high-dimensional discrete-attribute data sets. IEEE Transactions on Knowledge and Data Engineering 17 4 (2005) 447-461
14. Krishna K., and Murty M. Genetic K-means algorithm. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics 29 3 (1999) 433-439
15. Kuo, R.J., Cha, C.L., Chou, S.H., Shih, C.W., & Chiu, C.Y. (2003). Integration of ant algorithm and case based reasoning for knowledge management. In Proceedings of International Conference on IJIE, November 10-12, 2003, Las Vegas, USA, in CD-R.
16. Kuo, R. J., & Shih, C. W. (accepted for publication). Association rule mining through the ant colony system for national health insurance research database in Taiwan. Computers and Mathematics with Applications.
17. Kuo R.J., Wang H.S., Hu T.-L., and Chou S.H. Application of ant K-means on clustering analysis in data mining. International Journal of Computers and Mathematics with Applications 50 (2005) 1709-1724
18. Lin D., and Kedem Z. Pincer-search: an efficient algorithm for discovering the maximum frequent set. IEEE Transactions on Knowledge and Data Engineering 14 3 (2002) 553-566
19. Maulik H., and Bandyopadhyay S. Genetic algorithm-based clustering technique. Pattern Recognition 33 (2000) 1455-1465
20. Peacock Peter R. Data mining in marketing: Part 1. Marketing Management (1998) 9-18
21. Savasere, A., Omiecinski, E., & Navathe, S. (1995). An efficient algorithm for mining associate rules in large databases. In Proceedings of the international conference on very large data bases, Zurich, Switzerland (pp. 432-444).
22. Shih, C. W. (2004). Applying ant colony system in data mining under multi-dimensional constraints. Master Thesis of National Taipei University of Technology, Taiwan, ROC.
23. Su, B. D. (2002). Discovering association rules through ant systems. Master Thesis of National Chin-Hwa Univeristy, Taiwan, ROC.
24. Su M.C., and Chang H.T. Fast self-organizing feature map algorithm. IEEE Transactions on Neural Networks 11 3 (2000) 721-733
25. Su M.C., and Chang H.T. A new model of self-organizing neural networks and its application in data projection. IEEE Transactions on Neural Networks 12 (2001) 153-158
26. Toivonen, H. (1996). Sampling large databases for association rules. In Proceedings of the international conference on very large data bases, Mumbai (Bombay), India (pp. 134-145).
27. Tsai C.F., Wu H.C., and Tsai C.W. A new clustering approach for data mining in large databases. Proceedings of the international symposium on parallel architectures, algorithms and networks (ISPAN'02) (2002), IEEE Computer Society 1087-4089
28. Tsay Y.J., and Chang-Chien Y.W. An efficient cluster and decomposition algorithm for mining association rules. Information Sciences 160 (2004) 161-171
29. Witten I.H., and Frank E. Data mining: practical machine learning tools and techniques with java implementations (2000), Morgan Kaufmann Publishers
30. Yang, X. B., Sun, J. G., & Huang, D. (2002). A new clustering method based on ant colony algorithm. In Proceedings of the 4th world congress on intelligent control and automation, June (pp. 2222-2226).
31. Zhang, X., & Li, Y. (1993). Self-organizing map as a new method for clustering and data analysis. In Proc. IJCNN'93, int. joint conf. on neural networks (pp. 2448-2451).
32. Zaki M.J. Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering 12 3 (2000) 372-390