Wang Xiaoyan, Bai Yanping
School of Information and Communication Engineering, North University of China, Taiyuan 030051, China.
School of Science, North University of China, Taiyuan 030051, China.
Comput Intell Neurosci. 2016;2016:4606384. doi: 10.1155/2016/4606384. Epub 2016 Aug 30.
The MinMax -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 -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 -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 -means algorithm and the original MinMax -means algorithm. The experimental results indicate that our proposed algorithm can reach the lowest clustering errors automatically.
MinMax均值算法被广泛用于通过最小化最大簇内误差来应对初始化不佳的影响。执行过程涉及两个参数，即指数参数和记忆参数。由于不同参数具有不同的聚类误差，因此选择合适的参数至关重要。在原始算法中，给出了一个实用框架。该框架将MinMax均值算法进行扩展，以使其指数参数能够自动适应数据集。人们一直认为，如果设置了最大指数参数，那么程序就能达到最低的簇内误差。然而，我们的实验表明情况并非总是如此。在本文中，我们通过粒子群优化算法（PSO）对MinMax均值算法进行了改进，以确定能使算法达到最低聚类误差的参数合适值。所提出的聚类方法在几种不同初始情况下的一些常用数据集上进行了测试，并与均值算法和原始的MinMax均值算法进行了比较。实验结果表明，我们提出的算法能够自动达到最低的聚类误差。
