Evolutionary Techniques for Hierarchical Clustering Applied to Microarray Data

Autor:	Luis A. Miguel-Quintales, José A. Castellanos-Garzón
Rok vydání:	2008
Předmět:	Biological data ComputingMethodologies_PATTERNRECOGNITION Local optimum Computer science Genetic algorithm Gene chip analysis Evolutionary algorithm Combinatorial optimization Data mining Cluster analysis computer.software_genre computer Hierarchical clustering
Zdroj:	Advances in Soft Computing ISBN: 9783540858607 IWPACBB
DOI:	10.1007/978-3-540-85861-4_15
Popis:	In this paper we propose a novel hierarchical clustering method that uses a genetic algorithm based on mathematical proofs for the analysis of gene expression data, and show its effectiveness with regard to other clustering methods. The analysis of clusters with genetic algorithms has disclosed good results on biological data, and several studies have been carried out on the latter, although the majority of these researches have been focused on the partitional approach. On the other hand, the deterministic methods for hierarchical clustering generally converge to a local optimum. The method introduced here attempts to solve some of the problems faced by other hierarchical methods. The results of the experiments show that the method could be very effective in the cluster analysis on DNA microarray data.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::a29332e49f7f1057b12aa1b69bca68b8 https://doi.org/10.1007/978-3-540-85861-4_15 Zobrazit plný text záznamu