A Novel Similarity Measure Between Two Probability Distributions For Course Establishment

DOI

10.2991/emcs-15.2015.57How to use a DOI?

Keywords

Description length; Data Mining; K-means; Course establishment

Abstract

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

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  - 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  -