Paper Info
Title
A competitive co-evolving support vector clustering
Abstract
The goal of clustering is to cluster the objects into groups that are internally homogeneous and heterogeneous from group to group. Clustering is an important tool for diversely intelligent systems. So, many works have been researched in the machine learning algorithms. But, some problems are still shown in the clustering. One of them is to determine the optimal number of clusters. In K-means algorithm, the number of cluster K is determined by the art of researchers. Another problem is an over fitting of learning models. The majority of learning algorithms for clustering are not free from the problem. Therefore, we propose a competitive co-evolving support vector clustering. Using competitive co-evolutionary computing, we overcome the over fitting problem of support vector clustering which is a good learning model for clustering. The number of clusters is efficiently determined by our competitive co-evolving support vector clustering. To verify the improved performances of our research, we compare competitive co-evolving support vector clustering with established clustering methods using the data sets form UCI machine learning repository.
Year
DOI
Venue
2006
10.1007/11893028_96
ICONIP (1)
Keywords
Field
DocType
fitting problem,support vector clustering,established clustering method,cluster k,optimal number,k-means algorithm,uci machine,good learning model,competitive co-evolving support vector,competitive co-evolutionary computing,machine learning,evolutionary computing,k means algorithm
Canopy clustering algorithm,Fuzzy clustering,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Computer science,Artificial intelligence,Constrained clustering,Cluster analysis,Machine learning,Single-linkage clustering
Conference
Volume
ISSN
ISBN
4232
0302-9743
3-540-46479-4
Citations 
PageRank 
References 
2
0.39
15
Authors
2
Name
Order
Citations
PageRank
Sung-Hae Jun19511.79
Kyung-Whan Oh2203.20