Evolutionary Composite Attribute Clustering

Author

Hong, Tzung-Pei ; Song, Wei-Ping ; Chiu, Chu-Tien

Author_Institution

Dept. of Comput. Sci. & Inf. Eng., Nat. Univ. of Kaohsiung, Kaohsiung, Taiwan

fYear

2011

fDate

11-13 Nov. 2011

Firstpage

305

Lastpage

308

Abstract

In this paper, we propose a GA-based clustering method for composite-attribute clustering and feature selection. We have also designed a new chromosome representation, a fitness evaluation function, and an adjustment process in the proposed approach. Experimental results show that the proposed approach with composite attributes performs better than that without composite attributes.

Keywords

data mining; genetic algorithms; pattern clustering; GA-based clustering method; adjustment process; chromosome representation; evolutionary composite attribute clustering; feature selection; fitness evaluation function; Accuracy; Biological cells; Computer science; Educational institutions; Genetic algorithms; Genetics; Machine learning; Classification; Composite Attribute; Feature Clustering; Feature Selection; Genetic Algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Technologies and Applications of Artificial Intelligence (TAAI), 2011 International Conference on

Conference_Location

Chung-Li

Print_ISBN

978-1-4577-2174-8

Type

conf

DOI

10.1109/TAAI.2011.59

Filename

6120762

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2896656