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Implementing Text Clustering by Compound Evolutionary Algorithm
QIAO Yingying #,SONG Wei *
School of Internet of Things Engineering, Jiangnan University
*Correspondence author
#Submitted by
Subject:
Funding: Natural Science Foundation of Jiangsu Province （No.SBK201122266）, National Natural Science Foundation of China （No.61103129）, the Specialized Research Fund for the Doctoral Program of Higher Education （No.20100093120004）
Opened online:30 October 2013
Accepted by: none
Citation: QIAO Yingying,SONG Wei.Implementing Text Clustering by Compound Evolutionary Algorithm[OL]. [30 October 2013] http://en.paper.edu.cn/en_releasepaper/content/4565293
 
 
In order to solve the problem of premature convergence and limitations in optimization of single clustering algorithm, this paper proposes a new clustering algorithm combining GA and QPSO for high-dimensional text clustering. Using robust global optimization performance of GA to initialize particles in QPSO, the searching efficiency and clustering performance of QPSO algorithm is improved by giving full play to the advantages of the two algorithms. We conducted the experiments on the 20Newsgroup dataset and the experiments results showed that our method outperforms the sole GA or QPSO method and achieved high text clustering accuracy and effectiveness measured by the precision, recall and F-measure with tolerable time consumption.
Keywords:text clustering; GA clustering; QPSO clustering; PSO clustering
 
 
 

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