Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Mining fuzzy association rules in databases
ACM SIGMOD Record
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
ACM SIGKDD Explorations Newsletter
Efficient Adaptive-Support Association Rule Mining for Recommender Systems
Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
Web Mining: Information and Pattern Discovery on the World Wide Web
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
A Fuzzy Approach for Mining Quantitative Association Rules
A Fuzzy Approach for Mining Quantitative Association Rules
Web usage mining: discovery and application of interesting patterns from web data
Web usage mining: discovery and application of interesting patterns from web data
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The association rules model is one of most widely used models in data mining. An association rule is an implication of the form X → Y, where X and Y are a set of items that satisfy two constraints, given by the user, called minimum support (minsup) and minimum confidence (minconf). Normally, the values of minsup and minconf are crisp. In this paper, we analyze how association rules mining is affected when these values are treated as fuzzy. In order to calculate frequent itemsets and to generate association rules, an algorithm based on fuzzy sets is proposed. Using the fuzzy inference system, FUZZYC, the algorithm offers to user an intuitive way for defining and tuning the minconf and minsup parameters.