A K-harmonic Means Clustering Algorithm Based on Enhanced Differential Evolution

The conventional K-harmonic means is tend to be trapped by local optima. To resolve this problem, a novel K-harmonic means clustering algorithm using enhanced differential evolution technique is proposed. This algorithm improves the global search ability by applying Laplace mutation operator and log...

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Bibliographic Details
Published in2013 Fifth International Conference on Measuring Technology and Mechatronics Automation pp. 13 - 16
Main Authors Lidong Zhang, Li Mao, Huaijin Gong, Hong Yang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.01.2013
Subjects
Algorithm design and analysis
Clustering algorithms
Convergence
differential evolution
Heuristic algorithms
K-harmonic means
Laplace mutation operator
logarithmically crossover probability
Sociology
Statistics
Vectors
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Summary:The conventional K-harmonic means is tend to be trapped by local optima. To resolve this problem, a novel K-harmonic means clustering algorithm using enhanced differential evolution technique is proposed. This algorithm improves the global search ability by applying Laplace mutation operator and logarithmically crossover probability operator. Numerical experiments show that this algorithm overcomes the disadvantages of the K-harmonic means, and improves the global search ability.
ISBN:9781467356527
1467356522
ISSN:2157-1473
DOI:10.1109/ICMTMA.2013.1