Application and Research on an Improved Clustering Method in Teaching Evaluation

DOI

10.2991/icemc-16.2016.220How to use a DOI?

Keywords

Teaching evaluation; Weight; K-medoids clustering; Evaluation index; Teaching quality

Abstract

In order to analyze the teaching evaluation data effectively, based on the diversity of the weight of different evaluation indexes, this paper focuses on the issue of the current evaluation index weight setting, the method of combined weight distribution is proposed. In order to improve the accuracy of clustering, the weight is introduced to the nearest neighbor K-medoids clustering algorithm. The experimental results of UCI data set and the teaching evaluation data show that the proposed algorithm is feasible and effective in the teaching evaluation data analysis.

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  - Shaorong Feng
PY  - 2016/05
DA  - 2016/05
TI  - Application and Research on an Improved Clustering Method in Teaching Evaluation
BT  - Proceedings of the 2016 International Conference on Education, Management and Computer Science
PB  - Atlantis Press
SP  - 1138
EP  - 1144
SN  - 1951-6851
UR  - https://doi.org/10.2991/icemc-16.2016.220
DO  - 10.2991/icemc-16.2016.220
ID  - Feng2016/05
ER  -