Name
Title | ||
|---|---|---|
An evolutionary computational model applied to cluster analysis of DNA microarray data |
Abstract | ||
|---|---|---|
This paper proposes a new hierarchical clustering method using genetic algorithms for the analysis of gene expression data. This method is based on the mathematical proof of several results, showing its effectiveness with regard to other clustering methods. Genetic algorithms applied to cluster analysis have disclosed good results on biological data and many studies have been carried out in this sense, although most of them are focused on partitional clustering methods. Even though there are few studies that attempt to use genetic algorithms for building hierarchical clustering, they do not include constraints that allow us to reduce the complexity of the problem. Therefore, these studies become intractable problems for large data sets. On the other hand, the deterministic hierarchical clustering methods generally face the problem of convergence towards local optimums due to their greedy strategy. The method introduced here is an alternative to solve some of the problems existing methods face. The results of the experiments have shown that our approach can be very effective in cluster analysis of DNA microarray data. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.eswa.2012.10.061 | Expert Syst. Appl. |
Keywords | Field | DocType |
partitional clustering method,gene expression data,genetic algorithm,hierarchical clustering,evolutionary computational model,biological data,clustering method,new hierarchical clustering method,deterministic hierarchical clustering method,dna microarray data,cluster analysis,combinatorial optimization,data mining | Hierarchical clustering,Data mining,Canopy clustering algorithm,Fuzzy clustering,CURE data clustering algorithm,Clustering high-dimensional data,Computer science,Consensus clustering,Artificial intelligence,Cluster analysis,Brown clustering,Machine learning | Journal |
Volume | Issue | ISSN |
40 | 7 | 0957-4174 |
Citations | PageRank | References |
9 | 0.74 | 33 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| José A. Castellanos-Garzón | 1 | 75 | 7.64 |
| Fernando DíAz | 2 | 30 | 2.53 |