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

被引：22

作者：

Goyal M. ^{[1
]}

Kumar S. ^{[1
]}

机构：

[1] Faculty of Engineering and Technology, Manav Rachna International University, Faridabad

来源：

Journal of The Institution of Engineers (India): Series B | 2014年 / 95卷 / 04期

关键词：

Clustering; Initial centroids; k-means algorithm; Partitional clustering algorithm;

D O I：

10.1007/s40031-014-0106-z

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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. © 2014, The Institution of Engineers (India).

引用

页码：345 / 350

页数：5

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