Application and Research on an Improved Clustering Method in Teaching Evaluation
- 10.2991/icemc-16.2016.220How to use a DOI?
- Teaching evaluation; Weight; K-medoids clustering; Evaluation index; Teaching quality
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.
- © 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/).
Cite this article
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 -