K-means and Support Vector Machine in Electric Power Company Benchmarking Management

DOI

10.2991/icassr-15.2016.96How to use a DOI?

Keywords

K-means; SVM; Benchmarking Management; Electric Power Company

Abstract

In the electric power company benchmarking management, implementing classification of the enterprise, the clustering algorithm can set up the model enterprise. It’s very important for the benchmarking management in the electric power company. K-means, as unsupervised learning algorithm, is suitable for processing great sample data, while support vector machine(SVM), as supervised learning algorithm, needs a small number of training samples and is able to obtain the higher classification accuracy. Therefore, the paper presented a classification method based on the combination of SVM and K-means. Using K-means clustered index data first, and then chose some samples which were close to each cluster center as study samples to training SVM classifier and classified all the index data with SVM classifier. Consequently, illustration showed that K-means combined with SVM had higher accuracy than K-means, which testified the validity of it.

Copyright

© 2016, 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  - Hong-qing Zhang
PY  - 2016/08
DA  - 2016/08
TI  - K-means and Support Vector Machine in Electric Power Company Benchmarking Management
BT  - Proceedings of the 3d International Conference on Applied Social Science Research
PB  - Atlantis Press
SP  - 354
EP  - 357
SN  - 1951-6851
UR  - https://doi.org/10.2991/icassr-15.2016.96
DO  - 10.2991/icassr-15.2016.96
ID  - Zhang2016/08
ER  -