A K-Means Clustering Algorithm Based on Enhanced Differential Evolution

Li Mao; Huai Jin Gong; Xing Yang Liu

doi:10.4028/www.scientific.net/AMR.339.71

Paper Titles

In Situ Higher-Time Derivative of Temperature Sensors for Aerospace Heat Transfer
p.53

Study on On-Line Torque Telemeter System for Large Motor in Active Oilfield Service
p.59

Research of Thickness Dynamic Detection of Thin Sheets
p.63

The Intelligent Detection System of the Machineroomless Elevator Based on WSN
p.67

A K-Means Clustering Algorithm Based on Enhanced Differential Evolution
p.71

LonWorks Node Communicating on Twisted Pairs Based on Virtual Toke
p.76

An Angle Measuring System for Knife Blade Based on Laser and Electronic Clinometer
p.80

Design of an Innovative High-Torque Brake
p.84

Calculation Method of Intumescent Coatings of Fire Protection for Concrete-Filled Hollow Structural Steel Columns
p.88

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 339A K-Means Clustering Algorithm Based on Enhanced...

A K-Means Clustering Algorithm Based on Enhanced Differential Evolution

Abstract:

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.

You might also be interested in these eBooks

Advanced Manufacturing Systems, ICMPMT 2011

View Preview

Info:

Periodical:

Advanced Materials Research (Volume 339)

Pages:

71-75

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.339.71

Citation:

Cite this paper

Online since:

September 2011

Authors:

Li Mao, Huai Jin Gong, Xing Yang Liu

Keywords:

Cluster Analysis, Differential Evolution, Exponentially Increasing Crossover Probability, K-Means Cluster Algorithm, Laplace Distribution

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] MacQueen J. Some methods for classification and analysis of multi-variate observations[C]// Proc. of the 5th Berkeley Symposium on Mathematics Statistic Problem, 1967, 1: 281~297.

Google Scholar

[2] Jiayao Wang, Xueping Zhang, Haiyan Zhou. A Genetic K-means Algorithm for Spatial Clustering [J]. Computer Engineering, 2006, 32(3): 188-190. In Chinese.

Google Scholar

[3] Michael Laszlo, Sumitra Mukherjee.A genetic algorithm that exchanges neighboring centers for k-means clustering[J]. Pattern Recognition Letters, 2007, 28(16): 2359-2366.

DOI: 10.1016/j.patrec.2007.08.006

Google Scholar

[4] Omran M G H, Engelbrecht A P, Salman A. Dynamic clustering using particle swarm optimization with application in unsupervised image classification [J]. Proceedings of World Academy of Science, Engineering and Technology, 2005, 9(11): 199-204.

Google Scholar

[5] Storn R, Price K.Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[J].Journal of Global Optimization, 1997, 11(4):341-359.

Google Scholar

[6] Paterlini S, Krink T.High performance clustering with differential evolution[C]// Proc. of Congress on Evolutionary Computation, 2004, 2: 2004-2011.

DOI: 10.1109/cec.2004.1331142

Google Scholar

[7] Sudhakar G. Effective image clustering with differential evolution technique[J]. International Journal of Computer and Communication Technology, 2010, 2(1): 11-19.

Google Scholar

[8] Kuo-Tong Lan, Chun-Hsiung Lan.Notes on the distinction of Gaussian and Cauchy mutations[C]// Proc. of Eighth International Conference on Intelligent Systems Design and Applications, 2008: 272-277.

DOI: 10.1109/isda.2008.237

Google Scholar