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An Active Candidate Set Management Model on Association Rule Discovery using Database Trigger and Incremental Update Technique  

Hwang, Jeong-Hui (Dept. of Computer Science, Chungbuk National University)
Sin, Ye-Ho (Dept. of Computer Science, Chungbuk National University)
Ryu, Geun-Ho (Dept. of Computer Science, Chungbuk National University)
Publication Information
Journal of KIISE:Databases / v.29, no.1, 2002 , pp. 1-14 More about this Journal
Abstract
Association rule discovery is a method of mining for the associated item set on large databases based on support and confidence threshold. The discovered association rules can be applied to the marketing pattern analysis in E-commerce, large shopping mall and so on. The association rule discovery makes multiple scan over the database storing large transaction data, thus, the algorithm requiring very high overhead might not be useful in real-time association rule discovery in dynamic environment. Therefore this paper proposes an active candidate set management model based on trigger and incremental update mechanism to overcome non-realtime limitation of association rule discovery. In order to implement the proposed model, we not only describe an implementation model for incremental updating operation, but also evaluate the performance characteristics of this model through the experiment.
Keywords
Active Mining; Active Database; Active Rule; Trigger Model;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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