An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
k-means: a new generalized k-means clustering algorithm
Pattern Recognition Letters
Research on k-means Clustering Algorithm: An Improved k-means Clustering Algorithm
IITSI '10 Proceedings of the 2010 Third International Symposium on Intelligent Information Technology and Security Informatics
A new algorithm for initial cluster centers in k-means algorithm
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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Clustering in data analysis means data with similar features are grouped together within a particular valid cluster. Each cluster consists of data that are more similar among themselves and dissimilar to data of other clusters. Clustering can be viewed as an unsupervised learning concept from machine learning perspective. In this paper, we have proposed an effective method to obtain better clustering with much reduced complexity. We have evaluated the performances of the classical K-Means approach of data clustering and the proposed Far Efficient K-Means method. The accuracy of both these algorithms were examined taking several data sets taken from UCI [13] repository of machine learning databases. Their clustering efficiency has been compared in conjunction with two typical cluster validity indices, namely the Davies-Bouldin Index and the Dunn's Index for different number of clusters, and our experimental results demonstrated that the quality of clustering by proposed method is much efficient than K-Means algorithm when larger data sets with more number of attributes are taken into consideration.