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New Technology of Library and Information Service  2011, Vol. 27 Issue (9): 34-40    DOI: 10.11925/infotech.1003-3513.2011.09.06
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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
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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.
Key wordsK-means cluster      Cluster number      Text clustering      Recommending system     
Received: 12 July 2011      Published: 02 December 2011

TP18 G350


Cite this article:

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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