Highly Efficient Pattern Mining Based on Transaction Decomposition
Chapter, Peer reviewed
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Original versionIEEE 35th International Conference on Data Engineering (ICDE). 2019, 35 10.1109/ICDE.2019.00163
This paper introduces a highly efficient pattern mining technique called Clustering-Based Pattern Mining (CBPM). This technique discovers relevant patterns by studying the correlation between transactions in transaction databases using clustering techniques. The set of transactions are first clus-tered using the k-means algorithm, where highly correlated transactions are grouped together. Next, the relevant patterns are derived by applying a pattern mining algorithm to each cluster. We present two different pattern mining algorithms, one approximate and one exact. We demonstrate the efficiency and effectiveness of CBPM through a thorough experimental evaluation.