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

Author

Lidong Zhang ; Li Mao ; Huaijin Gong ; Hong Yang

Author_Institution

Jiangsu Eng. R&D Center for Inf. Fusion Software, Jiangyin, China

fYear

2013

fDate

16-17 Jan. 2013

Firstpage

13

Lastpage

16

Abstract

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.

Keywords

evolutionary computation; learning (artificial intelligence); pattern clustering; probability; K-harmonic means clustering algorithm; Laplace mutation operator; differential evolution technique; global search ability; logarithmically crossover probability operator; Algorithm design and analysis; Clustering algorithms; Convergence; Heuristic algorithms; Sociology; Statistics; Vectors; K-harmonic means; Laplace mutation operator; differential evolution; logarithmically crossover probability;

fLanguage

English

Publisher

ieee

Conference_Titel

Measuring Technology and Mechatronics Automation (ICMTMA), 2013 Fifth International Conference on

Conference_Location

Hong Kong

Print_ISBN

978-1-4673-5652-7

Type

conf

DOI

10.1109/ICMTMA.2013.1

Filename

6493658