A parallelk-means clustering algorithm based on redundance elimination and extreme points optimization employing MapReduce-Reference-Cited by-同舟云学术

A parallelk-means clustering algorithm based on redundance elimination and extreme points optimization employing MapReduce

Published:2017-03-23 Issue:20 Volume:29 Page:e4109
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency Computat.: Pract. Exper.

Author:

Tang Zhuo¹^ORCID,Liu Kunkun¹,Xiao Jinbo¹,Yang Li²,Xiao Zheng¹

Affiliation:

1. College of Information Science and Engineering; Hunan University; Hunan 410082 China

2. Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation; School of Computer and Communication Engineering, Changsha University of Science and Technology; Changsha 410004 P. R. China

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/cpe.4109/fullpdf

Reference43 articles.

1. K-means algorithms for functional data;García-Ródenas;Neurocomputing,2015

2. Adapting the right measures for K-means clustering

3. Energy-aware scheduling of mapreduce jobs for big data applications;Mashayekhy;IEEE Transactions on Parallel & Distributed Systems,2015

4. Fast K-Means Clustering for Very Large Datasets Based on MapReduce Combined with a New Cutting Method

5. Parallel iterative compilation

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