Distributed Document Clustering Analysis Based on a Hybrid Method

作     者：J.E.Judith J.Jayakumari 

作者机构：Noorul Islam Centre for Higher EducationKumaracoilIndia 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2017年第14卷第2期

页      面：131-142页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：distributed document clustering hadoop k-means PSO mapreduce 

摘      要：Clustering is one of the recently challenging tasks since there is an *** amount of data in scientific research and commercial applications. High quality and fast document clustering algorithms are in great demand to deal with large volume of data. The computational requirements for bringing such growing amount data to a central site for clustering are complex. The proposed algorithm uses optimal centroids for *** clustering based on Particle Swarm Optimization(PSO).PSO is used to take advantage of its global search ability to provide optimal centroids which aids in generating more compact clusters with improved accuracy. This proposed methodology utilizes Hadoop and Map Reduce framework which provides distributed storage and analysis to support data intensive distributed applications. Experiments were performed on Reuter s and RCV1 document dataset which shows an improvement in accuracy with reduced execution time.