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Exceptional Pattern Discovery

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Discriminative Pattern Discovery on Biological Networks

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

Abstract

This chapter is devoted to a discussion on exceptional pattern discovery, namely on scenarios, contexts, and techniques concerning the mining of patterns which are so rare or so frequent to be considered as exceptional and, then, of interest for an expert to shed lights on the domain. Frequent patterns have found broad applications in areas like association rule mining, indexing, and clustering [1, 20, 23]. The application of frequent patterns in classification also achieved some success in the classification of relational data [6, 13, 14, 19, 25], text [15], and graphs [7]. The part is organized as follows. First, the frequent pattern mining on classical datasets is presented. This is not directly related with the content of the present work, which is mainly oriented in finding discriminating patterns, but they represent the starting point. Subsequently, Sect. 3.2 describes scenarios where patterns are exploited to discriminate between populations. Sections 3.3 and 3.4 illustrate how to mine patterns on networks and on biological data, respectively.

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Authors and Affiliations

  1. University of Calabria, Calabria, Cosenza, Italy

    Fabio Fassetti

  2. University of Palermo, Palermo, Italy

    Simona E. Rombo

  3. University of Calabria, Calabria, Cosenza, Italy

    Cristina Serrao

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  1. Fabio Fassetti
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  2. Simona E. Rombo
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  3. Cristina Serrao
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Correspondence to Fabio Fassetti .

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Fassetti, F., Rombo, S.E., Serrao, C. (2017). Exceptional Pattern Discovery. In: Discriminative Pattern Discovery on Biological Networks. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-63477-7_3

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  • DOI: https://doi.org/10.1007/978-3-319-63477-7_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63476-0

  • Online ISBN: 978-3-319-63477-7

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