Immunodomaince based clonal selection clustering algorithm

Author

Liu, Ruochen ; Shen, Zhengchun ; Jiao, Licheng ; Zhang, Wei

Author_Institution

Key Lab. of Intell. Perception & Image Understanding of Minist. of Educ. of China, Xidian Univ., Xi´´an, China

fYear

2010

fDate

18-23 July 2010

Firstpage

1

Lastpage

7

Abstract

Based on clonal selection principle and the immunodominance theory, a new immune clustering algorithm, Immunodomaince based Clonal Selection Clustering Algorithm (ICSCA) is proposed in this paper. An immunodomaince operator is introduced to the clonal selection algorithm, which can realize on-line gaining prior knowledge and sharing information among different antibodies. The proposed method has been extensively compared with Fuzzy C-means (FCM), Genetic Algorithm based FCM (GAFCM) and Clonal Selection Algorithm based FCM (CSAFCM) over a test suit of several real life datasets and synthetic datasets. The result of experiment indicates the superiority of the ICSCA over FCM, GAFCM and CSAFCM on stability and reliability for its ability to avoid trapping in local optimum.

Keywords

fuzzy set theory; genetic algorithms; pattern clustering; fuzzy C-means; genetic algorithm; immune clustering algorithm; immunodomaince operator; immunodominance thoery based clonal selection clustering algorithm; Accuracy; Algorithm design and analysis; Clustering algorithms; Immune system; Immunity testing; Partitioning algorithms; Variable speed drives;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation (CEC), 2010 IEEE Congress on

Conference_Location

Barcelona

Print_ISBN

978-1-4244-6909-3

Type

conf

DOI

10.1109/CEC.2010.5586327

Filename

5586327