Title
A point symmetry-based clonal selection clustering algorithm and its application in image compression
Abstract
To cluster data set with the character of symmetry, a point symmetry-based clonal selection clustering algorithm (PSCSCA) is proposed in this paper. Firstly, an immune vaccine operator is introduced to the classical clonal selection algorithm, which can gain a priori knowledge of pending problems so as to accelerate the convergent speed. Secondly, a point symmetry-based similarity measure is used to evaluate the similarity between two samples. Finally, both kd-trees-based approximate nearest neighbor searching and k-nearest-neighbor consistency strategy is used to reduce the computation complexity and improve the clustering accuracy. In the experiments, first of all, four real-life data sets and four synthetic data sets are used to test the performance of PSCSCA. PSCSCA is also compared with multiple existing algorithms in terms of clustering accuracy and convergent speed. In addition, PSCSCA is applied to a real-world application, namely natural image compression, with good performance being obtained.
Year
DOI
Venue
2014
10.1007/s10044-013-0344-8
Pattern Analysis & Applications
Keywords
Field
DocType
Clustering,Clone selection,Point symmetry distance,Immune vaccine,Image compression
k-medians clustering,Fuzzy clustering,Canopy clustering algorithm,CURE data clustering algorithm,Data stream clustering,Pattern recognition,Correlation clustering,Artificial intelligence,Clonal selection algorithm,Cluster analysis,Mathematics,Machine learning
Journal
Volume
Issue
ISSN
17
3
1433-7541
Citations 
PageRank 
References 
0
0.34
29
Authors
5
Name
Order
Citations
PageRank
Ruochen Liu122720.16
Fei He23213.85
Jing Liu310.69
Wen-Ping Ma450352.88
Yangyang Li5439.03