A Novel Similarity Measure Between Two Probability Distributions For Course Establishment
Aijiao Liu, Yiping Zhang, Min Chen
Available Online January 2015.
- 10.2991/emcs-15.2015.57How to use a DOI?
- Description length; Data Mining; K-means; Course establishment
In this paper, in order to obtain the optimized analysis of clustering for the probability distributions, the increment of the description length is proposed to instead the relative entropy as the similarity measure between two probability distributions. Its corresponding features are also discussed in detail in this paper. As the improvement, the increment of description satisfies the symmetrical feature. On the basis of this similarity measure, K-means algorithm is employed to analysis the police training data and to influence the corresponding course establishment. The experiment results indicate that the proposed similarity measure can lead to better clustering results than some other previous similarity measure
- © 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/).
Cite this article
TY - CONF AU - Aijiao Liu AU - Yiping Zhang AU - Min Chen PY - 2015/01 DA - 2015/01 TI - A Novel Similarity Measure Between Two Probability Distributions For Course Establishment BT - Proceedings of the International Conference on Education, Management, Commerce and Society PB - Atlantis Press SP - 269 EP - 273 SN - 2352-5398 UR - https://doi.org/10.2991/emcs-15.2015.57 DO - 10.2991/emcs-15.2015.57 ID - Liu2015/01 ER -