Frequent pattern discovery (or FP discovery, FP mining, or Frequent itemset mining) is part of knowledge discovery in databases, Massive Online Analysis, and data mining; it describes the task of finding the most frequent and relevant patterns in large datasets.[1][2]
The concept was first introduced for mining transaction databases.[3]
Frequent patterns are defined as subsets (itemsets, subsequences, or substructures) that appear in a data set with frequency no less than a user-specified or auto-determined threshold.[2][4]
^Jiawei Han; Hong Cheng; Dong Xin; Xifeng Yan (2007). "Frequent pattern mining: current status and future directions" (PDF). Data Mining and Knowledge Discovery. 15: 55–86. doi:10.1007/s10618-006-0059-1. S2CID 8085527. Retrieved 2019-01-31.
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