A Modified MinMax k-Means Algorithm Based on PSO

The MinMax k -means algorithm is widely used to tackle the effect of bad initialization by minimizing the maximum intraclustering errors. Two parameters, including the exponent parameter and memory parameter, are involved in the executive process. Since different parameters have different clustering...

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Bibliographic Details
Published in:Computational Intelligence and Neuroscience Vol. 2016; no. 2016; pp. 928 - 940-077
Main Authors: Wang, Xiao-Yan, Bai, Yanping
Format: Journal Article
Language:English
Published: Cairo, Egypt Hindawi Limiteds 2016
Hindawi Publishing Corporation
John Wiley & Sons, Inc
Subjects:
Algorithms
Clustering
Colleges & universities
Datasets
Errors
Experiments
Exponents
Intelligence
Mathematical optimization
Mathematical research
Parameter modification
Parameters
ISSN:1687-5265, 1687-5273
Online Access:Get full text
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Summary:The MinMax k -means algorithm is widely used to tackle the effect of bad initialization by minimizing the maximum intraclustering errors. Two parameters, including the exponent parameter and memory parameter, are involved in the executive process. Since different parameters have different clustering errors, it is crucial to choose appropriate parameters. In the original algorithm, a practical framework is given. Such framework extends the MinMax k -means to automatically adapt the exponent parameter to the data set. It has been believed that if the maximum exponent parameter has been set, then the programme can reach the lowest intraclustering errors. However, our experiments show that this is not always correct. In this paper, we modified the MinMax k -means algorithm by PSO to determine the proper values of parameters which can subject the algorithm to attain the lowest clustering errors. The proposed clustering method is tested on some favorite data sets in several different initial situations and is compared to the k -means algorithm and the original MinMax k -means algorithm. The experimental results indicate that our proposed algorithm can reach the lowest clustering errors automatically.
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Academic Editor: Stefano Squartini
ISSN:1687-5265
1687-5273
DOI:10.1155/2016/4606384