Frequent pattern discovery
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[edit]
Techniques for FP mining include:
- market basket analysis[3]
- cross-marketing
- catalog design
- clustering
- classification
- recommendation systems[1]
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]
References[edit]
- ^ 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.
- ^ a b "Frequent Pattern Mining". SIGKDD. 1980-01-01. Retrieved 2019-01-31.
- ^ 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 10.1.1.217.4132. doi:10.1145/170036.170072. ISSN 0163-5808.
- ^ "Frequent pattern Mining, Closed frequent itemset, max frequent itemset in data mining". T4Tutorials. 2018-12-09. Retrieved 2019-01-31.
- ^ "Frequent Pattern Mining". Spark 2.4.0 Documentation. Retrieved 2019-01-31.