Frequent pattern discovery
Frequent pattern discovery (FP discovery, FP mining, or Frequent itemset mining) as part of knowledge discovery in databases / Massive Online Analysis, and data mining describes the task of finding the most frequent and relevant patterns in large datasets. The concept was first introduced for mining transaction databases. Frequent patterns are defined as subsets (itemsets, subsequences, or substructures) that that appear in a data set with frequency no less than a user-specified or auto-determined threshold.
Techniques for FP mining include:
- market basket analysis
- catalog design
- recommendation systems
and respective specific techniques.
- Jiawei Han; Hong Cheng; Dong Xin; Xifeng Yan (2007). "Frequent pattern mining: current status and futuredirections" (PDF). Data Mining and Knowledge Discovery. 15: 55–86. doi:10.1007/s10618-006-0059-1. Retrieved 2019-01-31.
- "Frequent Pattern Mining". SIGKDD. 1980-01-01. Retrieved 2019-01-31.
- 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.