The Three Steps of Clustering in the Post-Genomic Era: A Synopsis

Raffaele Giancarlo, Giosue' Lo Bosco, Utro, Filippo Utro, Luca Pinello

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)

Abstract

Clustering is one of the most well known activities in scientific investigation and the object of research in many disciplines, ranging from Statistics to Computer Science. Following Handl et al., it can be summarized as a three step process: (a) choice of a distance function; (b) choice of a clustering algorithm; (c) choice of a validation method. Although such a purist approach to clustering is hardly seen in many areas of science, genomic data require that level of attention, if inferences made from cluster analysis have to be of some relevance to biomedical research. Unfortunately, the high dimensionality of the data and their noisy nature makes cluster analysis of genomic data particularly difficult. This paper highlights new findings that seem to address a few relevant problems in each of the three mentioned steps, both in regard to the intrinsic predictive power of methods and algorithms and their time performance. Inclusion of this latter aspect into the evaluation process is quite novel, since it is hardly considered in genomic data analysis.
Original languageEnglish
Title of host publicationComputational Intelligence Methods for Bioinformatics and Biostatistics, 7th International Meeting, CIBB 2010, Palermo, Italy, September 2010 Revised Selected Papers
Pages13-30
Number of pages18
Publication statusPublished - 2011

Publication series

NameLECTURE NOTES IN COMPUTER SCIENCE

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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