Efficient Clustering of Dataset Based on Differential Evolution

Conference paper

DOI: 10.1007/978-3-319-02931-3_26

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 247)
Cite this paper as:
Naik A., Satapathy S.C. (2014) Efficient Clustering of Dataset Based on Differential Evolution. In: Satapathy S., Udgata S., Biswal B. (eds) Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2013. Advances in Intelligent Systems and Computing, vol 247. Springer, Cham

Abstract

A novel approach to combining feature selection and clustering is presented. It uses selection of weighted Principal Components for features selection and automatic clustering based on Improved DE for clustering in order to reduce the complexity of high dimensional datasets and speed up the DE clustering process. We report significant improvements in total runtime. Moreover, the clustering accuracy of the dimensionality reduction DE clustering algorithm is comparable to the one that uses full dimensional datasets. The efficiency of this approach has been demonstrated with some real life datasets.

Keywords

Clustering PCs Dimension DE 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.MITSGwaliorIndia
  2. 2.Dept of Computer Science and EngineeringANITSThagarapuvalasaIndia

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