title: A Modified MinMaxk-Means Algorithm Based on PSO.
abstract: The MinMaxk-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 MinMaxk-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 MinMaxk-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 thek-means algorithm and the original MinMaxk-means algorithm. The experimental results indicate that our proposed algorithm can reach the lowest clustering errors automatically.
authors.affiliation: School of Information and Communication Engineering, North University of China, Taiyuan 030051, China.
authors.forename: Xiaoyan
authors.identifier: 0000-0002-6870-6048
authors.initials: X
authors.lastname: Wang
authors.affiliation: School of Science, North University of China, Taiyuan 030051, China.
authors.forename: Yanping
authors.identifier: 0000-0002-2043-8363
authors.initials: Y
authors.lastname: Bai