Association Rule Selection in a Data Mining Environment

Data mining methods easily produce large collections of rules, so that the usability of the methods is hampered by the sheer size of the rule set. One way of limiting the size of the result set is to provide the user with tools to help in finding the truly interesting rules. We use this approach in a case study where we search for association rules in NCHS health care data, and select interesting subsets of the result by using a simple query language implemented in the KESO data mining system. Our results emphasize the importance of the explorative approach supported by efficient selection tools.

[1]  Willi Klösgen,et al.  Explora: A Multipattern and Multistrategy Discovery Assistant , 1996, Advances in Knowledge Discovery and Data Mining.

[2]  Heikki Mannila,et al.  Finding interesting rules from large sets of discovered association rules , 1994, CIKM '94.

[3]  Abraham Silberschatz,et al.  What Makes Patterns Interesting in Knowledge Discovery Systems , 1996, IEEE Trans. Knowl. Data Eng..

[4]  Gregory Piatetsky-Shapiro,et al.  Discovery, Analysis, and Presentation of Strong Rules , 1991, Knowledge Discovery in Databases.

[5]  Heikki Mannila,et al.  Fast Discovery of Association Rules , 1996, Advances in Knowledge Discovery and Data Mining.

[6]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[7]  William Frawley,et al.  Knowledge Discovery in Databases , 1991 .

[8]  Arno Siebes,et al.  Data Mining and the KESO Project , 1996, SOFSEM.