Unsupervised gene selection and clustering using simulated annealing

Maurizio Filippone*, Francesco Masulli, Stefano Rovetta

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations


When applied to genomic data, many popular unsupervised explorative data analysis tools based on clustering algorithms often fail due to their small cardinality and high dimensionality. In this paper we propose a wrapper method for gene selection based on simulated annealing and unsupervised clustering. The proposed approach, even if computationally intensive, permits to select the most relevant features (genes), and to rank their relevance, allowing to improve the results of clustering algorithms.

Original languageEnglish (US)
Title of host publicationFuzzy Logic and Applications - 6th International Workshop, WILF 2005, Revised Selected Papers
Number of pages7
StatePublished - 2006
Event6th International Workshop - Fuzzy Logic and Applications - Crema, Italy
Duration: Sep 15 2005Sep 17 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3849 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference6th International Workshop - Fuzzy Logic and Applications

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


Dive into the research topics of 'Unsupervised gene selection and clustering using simulated annealing'. Together they form a unique fingerprint.

Cite this