Abstract
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.
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© 2015 Springer Science+Business Media Singapore
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Kim, B.W., Kim, J.M., Lee, W.G., Shon, J.G. (2015). Parallel Balanced Team Formation Clustering Based on MapReduce. In: Park, DS., Chao, HC., Jeong, YS., Park, J. (eds) Advances in Computer Science and Ubiquitous Computing. Lecture Notes in Electrical Engineering, vol 373. Springer, Singapore. https://doi.org/10.1007/978-981-10-0281-6_95
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DOI: https://doi.org/10.1007/978-981-10-0281-6_95
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