关键词: 聚类, k-均值算法, 误差平方和, 孤立点

Abstract: Traditional k-means algorithm is sensitive to initial clustering centers and isolated points, based on the principal of minimizing the sum of squared error to the most extent, an optimized k-means method is presented on selecting initial clustering centers. At the phase of initial selecting clustering centers, when adding a clustering point each time, compute reduced sum of squared error of each point and select the point that can maximize the square of the reduced error. Using real datasets and compared with the results of other algorithms, the experimental results show the number of iteration is reduced on selecting initial clustering centers and the quality of clustering is improved. Besides, artificial dataset demonstrates the method is much less sensitive to isolated points.

Key words: clustering, k-means algorithm, sum of squared error, isolated points