Proceedings of the 10th annual conference on Genetic and evolutionary computation
A novel distance measure for data vectors with nominal feature values
ECS'10/ECCTD'10/ECCOM'10/ECCS'10 Proceedings of the European conference of systems, and European conference of circuits technology and devices, and European conference of communications, and European conference on Computer science
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Data clustering is a process of putting similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is larger than among groups. This paper presents data clustering using improved genetic algorithm (IGA) and the popular Nelder-Mead(NM) Simplex search . To improve the accuracy of data clustering, an improved GA (IGA) is used. The performance of IGA is established with many benchmark test functions optimization. To accelerate the clustering process further more a hybrid algorithm based on improved GA and Nelder-Mead simplex search(NM) is suggested for clustering and is tested on 7 datasets and its performance is compared with above two algorithms and the traditional K-means algorithm. Keywords--Clustering, Improved Genetic Algorithm, K-means, Nelder-Mead, Hybrid algorithm