Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
CoMine: Efficient Mining of Correlated Patterns
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining rank-correlated sets of numerical attributes
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining quantitative correlated patterns using an information-theoretic approach
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
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We study the problem of mining correlated patterns. Correlated patterns have advantages over associations that they cover not only frequent items, but also rare items. Tight correlated item sets is a concise representation of correlated patterns, where items are correlated each other. Although finding such tight correlated item sets is helpful for applications, the algorithm's efficiency is critical, especially for high dimensional database. Thus, we first prove Lemma 1 and Lemma 2 in theory. Utilizing Lemma 1 and Lemma 2, we design an optimized RSC (Regional-Searching-Correlations) algorithm. Furthermore, we estimate the amount of pruned search space for data with various support distributions based on a probabilistic model. Experiment results demonstrate that RSC algorithm is much faster than other similar algorithms.