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Similarity Measures and Clustering of String Patterns

  • Chapter
Pattern Recognition and String Matching

Part of the book series: Combinatorial Optimization ((COOP,volume 13))

Abstract

Clustering is a powerful tool in revealing the intrinsic organization of data. A clustering of structural patterns consists of an unsupervised association of data based on the similarity of their structures and primitives. This chapter addresses the problem of structural clustering, and presents an overview of similarity measures used in this context. The distinction between string matching and structural resemblance is stressed. The hierarchical agglomerative clustering concept and a partitional approach are explored in a comparative study of several dissimilarity measures: minimum code length based measures; dissimilarity based on the concept of reduction in grammatical complexity; and error-correcting parsing.

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Author information

Authors and Affiliations

  1. Telecommunications Institute Instituto Superior Técnico, Technical University of Lisbon, Portugal

    Ana Fred

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  1. Ana Fred
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Editors and Affiliations

  1. University of Wisconsin — Green Bay, Green Bay, WI, USA

    Dechang Chen

  2. The George Washington University, Washington DC, USA

    Xiuzhen Cheng

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© 2003 Kluwer Academic Publishers

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Fred, A. (2003). Similarity Measures and Clustering of String Patterns. In: Chen, D., Cheng, X. (eds) Pattern Recognition and String Matching. Combinatorial Optimization, vol 13. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0231-5_7

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  • DOI: https://doi.org/10.1007/978-1-4613-0231-5_7

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7952-2

  • Online ISBN: 978-1-4613-0231-5

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