A query-driven interesting rule discovery using associations and spanning operations

Authors:
Jong P. Yoon;Larry Kerschberg
Affiliations:
University of Louisiana at Lafayette, The Center for Advanced Computer, Studies, Lafayette, LA;George Mason University, Department of Information and Software Engineering, Fairfax, VA
Venue:
Data mining, rough sets and granular computing
Year:
2002

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Abstract

In practice, users may often want interesting rules that are also related with user goals . This paper describes a technique of mining useful rules both interesting and related to user goals. According to the degree of relevancy to a user goal, a database can be divided into the five views: from the view positively related to the user goal to the view unrelated. To each such view, our novel technique of data mining can be applied. The union and join operations in SQL, unlike the traditional approaches which apply association and prunning operations to one view, are applied to one or more of those views. While the pattern association operation joins patterns over the different attributes, the pattern spanning operation unions patterns over the same attributes. The combination of two operations keeps both confidence and supportiveness measures together, and differenciation of query views enables us to produce the desired level of interestingness and relevancy.