Simultaneous feature selection and ant colony clustering

Clustering is a widely studied problem in data mining. Ai techniques, evolutionary techniques and optimization techniques are applied to this field. In this study, a novel hybrid modeling approach proposed for clustering and feature selection. Ant colony clustering technique is used to segment breast cancer data set. To remove irrelevant or redundant features from data set for clustering Sequential Backward Search feature selection technique is applied. Feature selection and clustering algorithms are incorporated as a Wrapper. The results show that, the accuracy of the FS-ACO clustering approach is better than the filter approaches.