首页|An Improved K-Means Algorithm Based on Initial Clustering Center Optimization
An Improved K-Means Algorithm Based on Initial Clustering Center Optimization
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The K-means algorithm is widely known for its simplicity and fastness in text clustering. However, the selection of the initial clus-tering center with the traditional K-means algorithm is some random, and therefore, the fluctuations and instability of the cluster-ing results are strongly affected by the initial clustering center. This paper proposed an algorithm to select the initial clustering center to eliminate the uncertainty of central point selection. The experiment results show that the improved K-means clustering algorithm is superior to the traditional algorithm.
clusteringK-means algorithminitial clustering center
LI Taihao、NAREN Tuya、ZHOU Jianshe、REN Fuji、LIU Shupeng
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Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China
Flatley Discovery Lab, Boston 02129, USA
Department of Information Science & Intelligent Systems, University of Tokushima, Tokushima 7708506, Japan
School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China
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