Pattern mining is a data mining setting aimed at finding frequently recurring structures in databases. The setting can be defined for many types of data, including binary attribute-value data (see Learning, Attribute-Value), graph data, and relational data (see Learning, Relational). The most common setting is one in which the database consists of a set S of instances of independent individuals and a pattern is defined to be any substructure which is included in a minimum number of instances in the set S. The extension of this setting toward other types of requirements has led to many other types of constraint-based pattern mining. Pattern mining distinguishes itself from pattern recognition in that the patterns found are usually discrete structures, instead of models with real-valued parameters.
Most patterns studied in the pattern mining literature are discrete objects; common pattern types include itemsets, sequences, graphs, and trees...
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