User-Defined Association Mining

Discovering interesting associations of events is an important data mining task. In many real applications, the notion of association, which defines how events are associated, often depends on the particular application and user requirements. This motivates the need for a general framework that allows the user to specify the notion of association of his/her own choices. In this paper we present such a framework, called the UDA mining (User-Defined Association Mining). The approach is to define a language for specifying a broad class of associations and yet efficient to be implemented. We show that (1) existing notions of association mining are instances of the UDA mining, and (2) many new ad-hoc association mining tasks can be defined in the UDA mining framework.

[1]  Padhraic Smyth,et al.  An Information Theoretic Approach to Rule Induction from Databases , 1992, IEEE Trans. Knowl. Data Eng..

[2]  Tomasz Imielinski,et al.  DataMine: Application Programming Interface and Query Language for Database Mining , 1996, KDD.

[3]  Giuseppe Psaila,et al.  A New SQL-like Operator for Mining Association Rules , 1996, VLDB.

[4]  Heikki Mannila,et al.  Methods and Problems in Data Mining , 1997, ICDT.

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

[6]  Ramakrishnan Srikant,et al.  Mining Association Rules with Item Constraints , 1997, KDD.

[7]  Rajeev Motwani,et al.  Beyond market baskets: generalizing association rules to correlations , 1997, SIGMOD '97.

[8]  T. Wickens.  Multiway Contingency Tables Analysis for the Social Sciences , 1989 .

[9]  Laks V. S. Lakshmanan,et al.  Exploratory mining and pruning optimizations of constrained associations rules , 1998, SIGMOD '98.

[10]  Chris Clifton,et al.  Query flocks: a generalization of association-rule mining , 1998, SIGMOD '98.

[11]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[12]  Jinyan Li,et al.  Efficient mining of emerging patterns: discovering trends and differences , 1999, KDD '99.

[13]  Heikki Mannila,et al.  A database perspective on knowledge discovery , 1996, CACM.

[14]  Philip S. Yu,et al.  A new framework for itemset generation , 1998, PODS '98.

[15]  Thomas D. Wickens,et al.  Multiway contingency table analysis for the social sciences. , 1989 .