Frequent pattern discovery

From Wikipedia, the free encyclopedia

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]


Techniques for FP mining include:

For the most part, FP discovery can be done using association rule learning with particular algorithms Eclat, FP-growth and the Apriori algorithm.

Other strategies include:

and respective specific techniques.

Implementations exist for various machine learning systems or modules like MLlib for Apache Spark.[5]


  1. ^ a b 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.
  2. ^ a b "Frequent Pattern Mining". SIGKDD. 1980-01-01. Retrieved 2019-01-31.
  3. ^ a b Agrawal, Rakesh; Imieliński, Tomasz; Swami, Arun (1993-06-01). "Mining association rules between sets of items in large databases". ACM SIGMOD Record. 22 (2): 207–216. CiteSeerX doi:10.1145/170036.170072. ISSN 0163-5808.
  4. ^ "Frequent pattern Mining, Closed frequent itemset, max frequent itemset in data mining". T4Tutorials. 2018-12-09. Retrieved 2019-01-31.
  5. ^ "Frequent Pattern Mining". Spark 2.4.0 Documentation. Retrieved 2019-01-31.