Improved Parallel Computation of PageRank for Web Searching

Dubey, Hema; Khare, Nilay; Kk, Appu Kuttan

doi:10.17485/ijst/2016/v9is1/102825

Cited by 1 publication

(1 citation statement)

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“…(Zhao,Ma,&He,2009)proposedaparallelk-meansalgorithmbasedonMapReduce named(PKMeans). (Yang,Mei,&Huang,2017)designedaparallelK-Meansalgorithmbasedon MapReduceinordertohaveagooddetectionofsatelliteimages.In (Dubey,Khare,Khare,&Narayan, 2016),anotherparallelk-meanspagerankalgorithmbasedonMapReduceforsolvingproblemsof previousimplementationsofPageRankisproposed.Theauthorsfocusedonreducingthecostof communicationwithintheclusterwhencomputingPageRankvaluesbyusingk-meansclusteringas agraphpartitioningmethodforsplittingthegraphintosubgraphsandbyimprovingtheexecution timebyusingacachememoryinordertoreducethenumberofjobs. (Lietal.,2011)proposeda methodnamedMBK-meanstosolveproblemsoftraditionalk-meanssuchasinstabilityandsensitivity tooutliersinclusteringonlargedatasets.Thismethodusesanensemblelearningmethodbagging.…”

Section: Related Workmentioning

confidence: 99%

Models for Internal Clustering Validation Indexes Based on Hadoop-MapReduce

Zerabi

Meshoul

Boucherkha

2020

International Journal of Distributed Systems and Technologies

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Cluster validation aims to both evaluate the results of clustering algorithms and predict the number of clusters. It is usually achieved using several indexes. Traditional internal clustering validation indexes (CVIs) are mainly based in computing pairwise distances which results in a quadratic complexity of the related algorithms. The existing CVIs cannot handle large data sets properly and need to be revisited to take account of the ever-increasing data set volume. Therefore, design of parallel and distributed solutions to implement these indexes is required. To cope with this issue, the authors propose two parallel and distributed models for internal CVIs namely for Silhouette and Dunn indexes using MapReduce framework under Hadoop. The proposed models termed as MR_Silhouette and MR_Dunn have been tested to solve both the issue of evaluating the clustering results and identifying the optimal number of clusters. The results of experimental study are very promising and show that the proposed parallel and distributed models achieve the expected tasks successfully.

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Section: Related Workmentioning

confidence: 99%