Title: Association-rule knowledge discovery by using a fuzzy mining approach

Authors: Lingling Zhang, Yong Shi, Xinhua Yang

Addresses: School of Management, Graduate University of Chinese Academy of Sciences, Chinese Academy of Sciences Research Center on Data Technology and Knowledge Economy, Beijing 100080, China. ' Chinese Academy of Sciences Research Center on Data Technology and Knowledge Economy, Beijing 100080, China. ' Alliance-PKU Management Consulting Company Limited, Beijing 100080, China

Abstract: Due to increasing use of very large database and data warehouses, discovering useful knowledge from transactions is becoming an important research area. One of approaches is fuzzy classification. Hong and Lee (1996) proposed a learning method that automatically derives fuzzy if-then rules from a set of given training examples using a decision table. Hong and Chen (1999) improved it. Based on their heuristic algorithms and the well-known Apriori approach, this paper proposes a new fuzzy mining algorithm to explore association rules from given quantitative transactions. Experimental results on Iris data show that the proposed algorithm effectively induces more association rules.

Keywords: data mining; association rules; fuzzy classification; knowledge discovery; fuzzy mining; knowledge management; information retrieval.

DOI: 10.1504/IJBIDM.2006.010783

International Journal of Business Intelligence and Data Mining, 2006 Vol.1 No.4, pp.417 - 429

Published online: 30 Aug 2006 *

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