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 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:

• 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.^[5]