Evolutionary algorithms for clustering gene-expression data

Hruschka, Eduardo R., de Castro, Leandro, and Campello, Ricardo J.G.B. (2004) Evolutionary algorithms for clustering gene-expression data. In: Proceedings of the 2004 IEEE International Conference on Data Mining. pp. 403-406. From: ICDM 04: 4th IEEE Conference on Data Mining, 1-4 November 2004, Brighton, UK.

[img] PDF (Published Version) - Published Version
Restricted to Repository staff only

View at Publisher Website: http://dx.doi.org/10.1109/ICDM.2004.1007...


This work deals with the problem of automatically finding optimal partitions in bioinformatics datasets. We propose incremental improvements for a Clustering Genetic Algorithm (CGA), culminating in the Evolutionary Algorithm for Clustering (EAC). The CGA and its modified versions are evaluated in five gene-expression datasets, showing that the proposed EAC is a promising tool for clustering gene-expression data.

Item ID: 47604
Item Type: Conference Item (Research - E1)
ISBN: 978-0-7695-2142-8
Date Deposited: 08 Mar 2017 07:40
FoR Codes: 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%
Downloads: Total: 3
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page