.png){kind=icon}
Return
Immunodomaince based Clonal Selection Clustering Algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2011.08.042.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. Firstly, by introducing a new immunodomaince operator to Clonal Selection Algorithm (CSA), the gene of elites in antibody population can be extracted and generalized to ordinary antibodies so as to gain on-line priori knowledge and share information among individuals. Then, one iteration of Fuzzy C-means clustering algorithm (FCM) and adaptive updating mechanism of antibody population are utilized to improve the diversity of antibody population in order to speed up the convergence speed. The proposed method has been extensively compared with FCM, GA-clustering algorithm (GACA) and Clonal Selection Algorithm based FCM (CSAFCM) over a test suit of several real life data sets and synthetic data sets. Experimental results indicate the superiority of the ICSCA over FCM, GAFCM and CSAFCM on clustering accuracy and robustness. (C) 2011 Elsevier B. V. All rights reserved.
Keywords:
Clone selection
Genetic algorithm
Immunodominance
Fuzzy clustering
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.6
Papers:
1.4W
Citations:
4.8W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSaveShareSaveRespiratory burst activity in brain macrophages: a flow cytometric study on cultured rat microglia
PREAI
ShareSave
View more