Tight Correlated Item Sets and Their Efficient Discovery
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.
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