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Frequent Pattern Mining

Curated by: Xifeng Yan

Frequent patterns are itemsets, subsequences, or substructures that appear in a data set with frequency no less than a user-specified threshold. For example, a set of items, such as milk and bread, that appear frequently together in a transaction data set, is a frequent itemset. A subsequence, such as buying first a PC, then a digital camera, and then a memory card, if it occurs frequently in a shopping history database, is a (frequent) sequential pattern. A substructure can refer to different structural forms, such as subgraphs, subtrees, or sublattices, which may be combined with itemsets or subsequences. If a substructure occurs frequently in a graph database, it is called a (frequent) structural pattern. Finding frequent patterns plays an essential role in mining associations, correlations, and many other interesting relationships among data. Moreover, it helps in data indexing, classification, clustering, and other data mining tasks as well. Frequent pattern mining is an important data mining task and a focused theme in data mining research. Abundant literature has been dedicated to this research and tremendous progress has been made, ranging from efficient and scalable algorithms for frequent itemset mining in transaction databases to numerous research frontiers, such as sequential pattern mining, structured pattern mining, correlation mining, associative classification, and frequent pattern-based clustering, as well as their broad applications [1]. A few text books are available on this topic, e.g., [2].

[1] Frequent Pattern Mining: Current Status and Future Directions, by J. Han, H. Cheng, D. Xin and X. Yan, 2007 Data Mining and Knowledge Discovery archive, Vol. 15 Issue 1, pp. 55 – 86, 2007

[2] Frequent Pattern Mining, Ed. Charu Aggarwal and Jiawei Han, Springer, 2014.

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