{{short description|Technique for database mining}} <!-- Note: The following pages were redirects to Frequent_pattern_discovery before draftification: *Frequent pattern *Frequent pattern mining --> '''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.<ref name=xyan>{{cite journal|url=https://www.cs.ucsb.edu/~xyan/papers/dmkd07_frequentpattern.pdf|doi=10.1007/s10618-006-0059-1|title=Frequent pattern mining: current status and future directions|journal=Data Mining and Knowledge Discovery|volume=15|pages=55–86| access-date=2019-01-31|author1=Jiawei Han|author2=Hong Cheng|author3=Dong Xin|author4=Xifeng Yan|year=2007|s2cid=8085527|doi-access=free}}</ref><ref name="sigkdd1980"/> The concept was first introduced for mining transaction databases.<ref name=agarwal1993/> 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.<ref name="sigkdd1980">{{cite web | title=Frequent Pattern Mining | website=SIGKDD | date=1980-01-01 | url=https://www.kdd.org/kdd2016/topics/view/frequent-pattern-mining | access-date=2019-01-31}}</ref><ref name=t4tutorials>{{cite web | title=Frequent pattern Mining, Closed frequent itemset, max frequent itemset in data mining | website=T4Tutorials | date=2018-12-09 | url=https://t4tutorials.com/frequent-pattern-mining-in-data-mining/ | ref={{sfnref | T4Tutorials | 2018}} | access-date=2019-01-31}}</ref>
==Techniques== Techniques for FP mining include:
* market basket analysis<ref name="agarwal1993">{{cite journal | last1=Agrawal | first1=Rakesh | last2=Imieliński | first2=Tomasz | last3=Swami | first3=Arun | title=Mining association rules between sets of items in large databases | journal=ACM SIGMOD Record | volume=22 | issue=2 | date=1993-06-01 | issn=0163-5808 | doi=10.1145/170036.170072 | pages=207–216 | citeseerx=10.1.1.217.4132 }}</ref> * cross-marketing * catalog design * clustering * classification * recommendation systems<ref name="xyan" />
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:
*Frequent subtree mining *Structure mining *Sequential pattern mining and respective specific techniques.
Implementations exist for various machine learning systems or modules like MLlib for Apache Spark.<ref name=spark>{{cite web | title=Frequent Pattern Mining | website=Spark 2.4.0 Documentation | url=https://spark.apache.org/docs/latest/ml-frequent-pattern-mining.html | ref={{sfnref | Spark 2.4.0 Documentation}} | access-date=2019-01-31}}</ref>
==References== {{reflist}}
Category:Data mining Category:Cluster analysis