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CLC number: TP301.6
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Received: 2006-03-15
Revision Accepted: 2006-05-11
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An efficient enhanced k-means clustering algorithm
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FAHIM A.M., SALEM A.M., TORKEY F.A., RAMADAN M.A.. An efficient enhanced k-means clustering algorithm[J]. Journal of Zhejiang University Science A, 2006, 7(10): 1626-1633.
@article{title="An efficient enhanced k-means clustering algorithm",
author="FAHIM A.M., SALEM A.M., TORKEY F.A., RAMADAN M.A.",
journal="Journal of Zhejiang University Science A",
volume="7",
number="10",
pages="1626-1633",
year="2006",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2006.A1626"
}
%0 Journal Article
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%A FAHIM A.M.
%A SALEM A.M.
%A TORKEY F.A.
%A RAMADAN M.A.
%J Journal of Zhejiang University SCIENCE A
%V 7
%N 10
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%@ 1673-565X
%D 2006
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.A1626
TY - JOUR
T1 - An efficient enhanced k-means clustering algorithm
A1 - FAHIM A.M.
A1 - SALEM A.M.
A1 - TORKEY F.A.
A1 - RAMADAN M.A.
J0 - Journal of Zhejiang University Science A
VL - 7
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SP - 1626
EP - 1633
%@ 1673-565X
Y1 - 2006
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2006.A1626
Abstract: In k-means clustering, we are given a set of n data points in d-dimensional space Rd and an integer k and the problem is to determine a set of k points in Rd, called centers, so as to minimize the mean squared distance from each data point to its nearest center. In this paper, we present a simple and efficient clustering algorithm based on the k-means algorithm, which we call enhanced k-means algorithm. This algorithm is easy to implement, requiring a simple data structure to keep some information in each iteration to be used in the next iteration. Our experimental results demonstrated that our scheme can improve the computational speed of the k-means algorithm by the magnitude in the total number of distance calculations and the overall time of computation.
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