Parallel balanced team formation clustering based on mapreduce

Byoung Wook Kim, Ja Mee Kim, Won Gyu Lee, Jin Gon Shon

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)


For effective cooperative learning grouping student is important. Grouping students can be generalized to the problem that clustering objects into some clusters from a computer science point of view. The large datasets, expensive task of clustering computationally and high dimensionality makes clustering of very large scale of data a challenging task. To effectively process very large datasets for clustering, parallel and distributed architectures have developed. MapReduce is a programming model that is used for handling large volumes of data over a distributed computing environment in parallel. In this paper, we present a Parallel Balanced Team Formation (PBTF) clustering algorithm for the MapReduce framework. The purpose of PBTF is to find partitions with high homogeneity in a group and high heterogeneity between groups in parallel.

Original languageEnglish
Title of host publicationLecture Notes in Electrical Engineering
PublisherSpringer Verlag
Number of pages5
Publication statusPublished - 2015

Publication series

NameLecture Notes in Electrical Engineering
ISSN (Print)18761100
ISSN (Electronic)18761119


  • Grouping student
  • Mapreduce
  • Parallel
  • Team formation

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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  • Cite this

    Kim, B. W., Kim, J. M., Lee, W. G., & Shon, J. G. (2015). Parallel balanced team formation clustering based on mapreduce. In Lecture Notes in Electrical Engineering (Vol. 373, pp. 671-675). (Lecture Notes in Electrical Engineering; Vol. 373). Springer Verlag.