A Modified MinMax kappa-Means Algorithm Based on PSO

作者：Wang, Xiaoyan; Bai, Yanping^*

来源：Computational Intelligence and Neuroscience, 2016, 2016: 4606384.

DOI：10.1155/2016/4606384

• PARTICLE SWARM OPTIMIZATION
• K-MEANS ALGORITHM

The MinMax kappa-means algorithmis 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 kappa-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 kappa-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 kappa-means algorithm and the original MinMax kappa-means algorithm. The experimental results indicate that our proposed algorithm can reach the lowest clustering errors automatically.

• 出版日期2016
• 单位中北大学