A K-Means Clustering Algorithm Based on Enhanced Differential Evolution
2011 ◽
Vol 339
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pp. 71-75
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Keyword(s):
The conventional k-means algorithms are sensitive to the initial cluster centers, and tend to be trapped by local optima. To resolve these problems, a novel k-means clustering algorithm using enhanced differential evolution technique is proposed in this paper. This algorithm improves the global search ability by applying Laplace mutation operator and exponentially increasing crossover probability operator. Numerical experiments show that this algorithm overcomes the disadvantages of the conventional k-means algorithms, and improves search ability with higher accuracy, faster convergence speed and better robustness.
2014 ◽
Vol 926-930
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pp. 3463-3466
Keyword(s):
2020 ◽
Vol 13
(6)
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pp. 168-178
2015 ◽
Vol 9
(1)
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pp. 65
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Keyword(s):
2015 ◽
Vol 2015
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pp. 1-21
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2021 ◽
Vol 12
(4)
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pp. 169-185
2019 ◽
Vol 12
(4)
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pp. 389-397
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