An Improved Method for Determining Optimal Number of Clusters in K-means Clustering Algorithm
Bian Peng1,2, Zhao Yan3, Su Yuzhao1,2
1. National Science Library, Chinese Academy of Sciences, Beijing 100190, China;
2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China;
3. Computer Science and Application Department, Zhengzhou Institute of Aeronautical Industry Management, Zhengzhou 450015, China
Abstract：Based on the text clustering requirement from the embedded NSTL Recommending System, this paper researches on the BWP algorithm, and analyzes the shortage of the BWP. Then an improved algorithm is proposed to optimize the calculation of the distance within the single sample cluster. The improved algorithm enlarges the range of clusters number based on the BWP. Moreover, it changes the partial optimum into the whole optimum. At last, the test result shows it is effective and efficient.
边鹏, 赵妍, 苏玉召. 一种改进的K-means算法最佳聚类数确定方法[J]. 现代图书情报技术, 2011, 27(9): 34-40.
Bian Peng, Zhao Yan, Su Yuzhao. An Improved Method for Determining Optimal Number of Clusters in K-means Clustering Algorithm. New Technology of Library and Information Service, 2011, 27(9): 34-40.
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