Exceptional Pattern Discovery
| Autor: | Simona E. Rombo, Fabio Fassetti, Cristina Serrao |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Biological data Point (typography) Association rule learning Computer science Relational database business.industry Search engine indexing computer.software_genre Domain (software engineering) Network pattern 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Artificial intelligence Cluster analysis business computer 030217 neurology & neurosurgery Natural language processing |
| Zdroj: | Discriminative Pattern Discovery on Biological Networks ISBN: 9783319634760 |
| DOI: | 10.1007/978-3-319-63477-7_3 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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