Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Machine Learning
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
The Journal of Machine Learning Research
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
An Improved Cluster Labeling Method for Support Vector Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Two-Phase Support Vector Clustering for Multi-Relational Data Mining
CW '05 Proceedings of the 2005 International Conference on Cyberworlds
Support vector machines based on K-means clustering for real-time business intelligence systems
International Journal of Business Intelligence and Data Mining
Cluster Analysis
Web usage mining using support vector machine
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
An overview of statistical learning theory
IEEE Transactions on Neural Networks
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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.