Algorithms for clustering data
Algorithms for clustering data
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
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Association rules over interval data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Integrated Computer-Aided Engineering
Mining frequent patterns from univariate uncertain data
Data & Knowledge Engineering
Weak Ratio Rules: A Generalized Boolean Association Rules
International Journal of Data Warehousing and Mining
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Mining association rules is an important problem in data mining. Algorithms for mining boolean data have been well studied and documented, but they cannot deal with quantitative and categorical data directly. For quantitative attributes, the general idea is partitioning the domain of a quantitative attribute into intervals, and applying boolean algorithms to the intervals. But, there is a conflict between the minimum support problem and the minimum confidence problem, while existing partitioning methods cannot avoid the conflict. Moreover, we expect the intervals to be meaningful. Clustering in data mining is a discovery process which groups a set of data such that the intracluster similarity is maximized and the intercluster similarity is minimized. The discovered clusters are used to explain the characteristics of the data distribution. The present paper will propose a novel method to find quantitative association rules by clustering the transactions of a database into clusters and projecting the clusters into the domains of the quantitative attributes to form meaningful intervals which may be overlapped. Experimental results show that our approach can efficiently find quantitative association rules, and can find important association rules which may be missed by the previous algorithms.