A clonal selection clustering algorithm using pointed symmetry-based distance measure

Authors:
Ruochen Liu;Hejun Ning;Wei Zhang;Licheng Jiao
Affiliations:
Institute of Intelligent Information Processing, Xidian University, Xi'an, China;Institute of Intelligent Information Processing, Xidian University, Xi'an, China;Institute of Intelligent Information Processing, Xidian University, Xi'an, China;Institute of Intelligent Information Processing, Xidian University, Xi'an, China
Venue:
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Year:
2010

Citing 5
Cited 0

A Robust Competitive Clustering Algorithm With Applications in Computer Vision

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Modified Version of the K-Means Algorithm with a Distance Based on Cluster Symmetry

IEEE Transactions on Pattern Analysis and Machine Intelligence
K-nearest-neighbor consistency in data clustering: incorporating local information into global optimization

Proceedings of the 2004 ACM symposium on Applied computing
GAPS: A clustering method using a new point symmetry-based distance measure

Pattern Recognition
A novel genetic algorithm based on immunity

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

Quantified Score

Hi-index	0.00

Visualization

Abstract

A clonal selection clustering algorithm using point symmetry-based distance measure (CSCAPS) is proposed in this paper, a point symmetry-based similarity measure is used to evaluate the similarity between two samples in order to cluster data sets with the character of symmetry. Both Kd-trees based nearest neighbor search and k-nearest-neighbor consistency strategy are used to reduce the computation complexity and improve the clustering accuracy. The proposed method has been extensively compared with four well-known clustering algorithms over a test suit of real life data sets and synthetic data sets. The results of experiments indicate the superiority of the CSCAPS on accuracy.