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
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
- ↑ 1.0 1.1 Jiawei Han; Hong Cheng; Dong Xin; Xifeng Yan (2007). "Frequent pattern mining: current status and future directions". Data Mining and Knowledge Discovery 15: 55–86. doi:10.1007/s10618-006-0059-1. https://www.cs.ucsb.edu/~xyan/papers/dmkd07_frequentpattern.pdf. Retrieved 2019-01-31.
- ↑ 2.0 2.1 "Frequent Pattern Mining". 1980-01-01. https://www.kdd.org/kdd2016/topics/view/frequent-pattern-mining.
- ↑ 3.0 3.1 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. doi:10.1145/170036.170072. ISSN 0163-5808.
- ↑ "Frequent pattern Mining, Closed frequent itemset, max frequent itemset in data mining". 2018-12-09. https://t4tutorials.com/frequent-pattern-mining-in-data-mining/.
- ↑ "Frequent Pattern Mining". https://spark.apache.org/docs/latest/ml-frequent-pattern-mining.html.
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