Encyclopedia of Systems Biology

2013 Edition
| Editors: Werner Dubitzky, Olaf Wolkenhauer, Kwang-Hyun Cho, Hiroki Yokota

Pattern Mining

  • Siegfried Nijssen
Reference work entry
DOI: https://doi.org/10.1007/978-1-4419-9863-7_600


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.


Pattern Structure

Most patterns studied in the pattern mining literature are discrete objects; common pattern types include itemsets, sequences, graphs, and trees...

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© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  1. 1.Katholieke UniversiteitLeuvenBelgium