Parallel MapReduce for Clustering of Residential Customers Energy Behavior

DOI

10.2991/icmmita-15.2015.210How to use a DOI?

Keywords

Mapreduce; Fuzzy c-means clustering; Analysis of electric behavior; Big data.

Abstract

In allusion to the problem about electricity behavior analysis in the low efficiency of dealing with huge amounts of data, we puts forward the Fuzzy c-means clustering (Fuzzy c-means clustering, FCM) parallel algorithm based on Mapreduce technology. By decomposing the iterative process of FCM algorithm into two steps of Map and Reduce, it can effectively improve the efficiency of similarity computing between the data objects and the clustering centers. On this basis, the four characteristics of resident electrical data are clustering analyzed by using the proposed FCM parallel algorithm. The experimental results show that the proposed algorithm can improve the efficiency of mass data clustering analysis and also proves the feasibility of the model.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zheng Wang
PY  - 2015/11
DA  - 2015/11
TI  - Parallel MapReduce for Clustering of Residential Customers Energy Behavior
BT  - Proceedings of the 2015 3rd International Conference on Machinery, Materials and Information Technology Applications
PB  - Atlantis Press
SP  - 1143
EP  - 1148
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmita-15.2015.210
DO  - 10.2991/icmmita-15.2015.210
ID  - Wang2015/11
ER  -