Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Induction of fuzzy rules and membership functions from training examples
Fuzzy Sets and Systems
Fast discovery of association rules
Advances in knowledge discovery and data mining
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Fuzzy data mining for interesting generalized association rules
Fuzzy Sets and Systems - Theme: Learning and modeling
Introduction To Business Data Mining
Introduction To Business Data Mining
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Data mining from 1994 to 2004: an application-orientated review
International Journal of Business Intelligence and Data Mining
ODAM: An Optimized Distributed Association Rule Mining Algorithm
IEEE Distributed Systems Online
Online analytical mining association rules using Chi-square test
International Journal of Business Intelligence and Data Mining
Fuzzy transform and least-squares approximation: Analogies, differences, and generalizations
Fuzzy Sets and Systems
A new parallel association rule mining algorithm on distributed shared memory system
International Journal of Business Intelligence and Data Mining
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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.