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Clustering Trend Predictions using Evolutionary k-means Algorithm for Automated Clustering

International Journal of Knowledge Based Computer Systems

Volume 1 Issue 2

Published: 2013
Author(s) Name: Jyoti Lakhani, Dharmesh Harwani | Author(s) Affiliation: Maharaja Ganga Singh University, Bikaner, Rajasthan, India
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Abstract

The paper proposed a method of hybridization of k-means algorithm and evolutionary programming. The blend of the two generates k number of clusters C = (c1, ..., ck) in the data space D = {x1, ..., xn}. These clusters will evolve in such a way that prediction of the upcoming trends of clusters in the application is possible. The proposed hybrid is named as evolutionary k-means clustering algorithm which is useful in generating and predicting clustering trends in an automated system.

Keywords: Clustering, Data Mining, Evolutionary Programming, K-Means

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