Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Knowledge Discovery in Databases
Knowledge Discovery in Databases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
A Fuzzy Approach for Mining Quantitative Association Rules
A Fuzzy Approach for Mining Quantitative Association Rules
Interestingness Measures for Fuzzy Association Rules
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Weighted Association Rule Mining from Binary and Fuzzy Data
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Fuzzy Weighted Association Rule Mining with Weighted Support and Confidence Framework
New Frontiers in Applied Data Mining
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During the last ten years, data mining, also known as knowledge discovery in databases, has established its position as a prominent and important research area. Mining association rules is one of the important research problems in data mining. Many algorithms have been proposed to find association rules in large databases containing both categorical and quantitative attributes. We generalize this to the case where part of attributes are given weights to reflect their importance to the user. In this paper, we introduce the problem of mining weighted quantitative association rules based on fuzzy approach. Using the fuzzy set concept, the discovered rules are more understandable to a human. We propose two different definitions of weighted support: with and without normalization. In the normalized case, a subset of a frequent itemset may not be frequent, and we cannot generate candidate k-itemsets simply from the frequent (k-1)-itemsets. We tackle this problem by using the concept of z-potential frequent subset for each candidate itemset. We give an algorithm for mining such quantitative association rules. Finally, we describe the results of using this approach on a real-life dataset.