Improving the Initial Centroids of k-means Clustering Algorithm to Generalize its Applicability

k-means is one of the most widely used partition based clustering algorithm. But the initial centroids generated randomly by the k-means algorithm cause the algorithm to converge at the local optimum. So to make k-means algorithm globally optimum, the initial centroids must be selected carefully rat...

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Veröffentlicht in:Journal of the Institution of Engineers (India). Series B, Electrical Engineering, Electronics and telecommunication engineering, Computer engineering Jg. 95; H. 4; S. 345 - 350
Hauptverfasser: Goyal, M., Kumar, S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: India Springer India 01.12.2014
Schlagworte:
Communications Engineering
Engineering
Networks
Original Contribution
Partitional clustering algorithm
Clustering
Initial centroids
k-means algorithm
ISSN:2250-2106, 2250-2114
Online-Zugang:Volltext
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Zusammenfassung:k-means is one of the most widely used partition based clustering algorithm. But the initial centroids generated randomly by the k-means algorithm cause the algorithm to converge at the local optimum. So to make k-means algorithm globally optimum, the initial centroids must be selected carefully rather than randomly. Though many researchers have already been carried out for the enhancement of k-means algorithm, they have their own limitations. In this paper a new method to formulate the initial centroids is proposed which results in better clusters equally for uniform and non-uniform data sets.
ISSN:2250-2106
2250-2114
DOI:10.1007/s40031-014-0106-z