Interestingness of Discovered Association Rules in Terms of Neighborhood-Based Unexpectedness

One of the central problems in knowledge discovery is the development of good measures of interestingness of discovered patterns. With such measures, a user needs to manually examine only the more interesting rules, instead of each of a large number of mined rules. Previous proposals of such measures include rule templates, minimal rule cover, actionability, and unexpectedness in the statistical sense or against user beliefs.

[1]  Abraham Silberschatz,et al.  On Subjective Measures of Interestingness in Knowledge Discovery , 1995, KDD.

[2]  Alexander Tuzhilin.  A Pattern Discovery Algebra , 1997, DMKD.

[3]  Jiawei Han,et al.  Meta-Rule-Guided Mining of Association Rules in Relational Databases , 1995, KDOOD/TDOOD.

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

[5]  Wynne Hsu,et al.  Post-Analysis of Learned Rules , 1996, AAAI/IAAI, Vol. 1.

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

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

[8]  Heikki Mannila,et al.  Pruning and grouping of discovered association rules , 1995 .

[9]  Tomasz Imielinski,et al.  Database Mining: A Performance Perspective , 1993, IEEE Trans. Knowl. Data Eng..

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

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

[12]  Gregory Piatetsky-Shapiro,et al.  The interestingness of deviations , 1994 .

[13]  Philip S. Yu,et al.  Data Mining: An Overview from a Database Perspective , 1996, IEEE Trans. Knowl. Data Eng..

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