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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Vydáno v:	Journal of the Institution of Engineers (India). Series B, Electrical Engineering, Electronics and telecommunication engineering, Computer engineering Ročník 95; číslo 4; s. 345 - 350
Hlavní autoři:	Goyal, M., Kumar, S.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	India Springer India 01.12.2014
Témata:	Communications Engineering Engineering Networks Original Contribution Partitional clustering algorithm Clustering Initial centroids k-means algorithm
ISSN:	2250-2106, 2250-2114
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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