Probability of large-scale data set EM clustering algorithms based on partial information constraints

DOI

10.2991/wartia-16.2016.346How to use a DOI?

Keywords

Some constraint information, Clustering, The data set ,The clustering quality, The probability of clustering algorithm

Abstract

The current situation, the need for clustering of data is very large, and the use of traditional algorithm for clustering process often tedious and time consuming is very long, the effect is not obvious. Based on this, this paper proposes a data sets EM probability based on some constraint information clustering algorithm, the detailed implementation process of the whole algorithm is described. Through experiment contrast scalable EM, positive_PC_SEM and full_PC_SEM clustering quality and efficiency of execution of the algorithm, the results show that the positive_PC_SEM algorithm and scalable EM algorithm compared to the clustering quality and efficiency is higher, although full_PC_SEM clustering quality is very high, but requires a lot of time.

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  - Xiao yan Liu
PY  - 2016/05
DA  - 2016/05
TI  - Probability of large-scale data set EM clustering algorithms based on partial information constraints
BT  - Proceedings of the 2016 2nd Workshop on Advanced Research and Technology in Industry Applications
PB  - Atlantis Press
SP  - 1746
EP  - 1749
SN  - 2352-5401
UR  - https://doi.org/10.2991/wartia-16.2016.346
DO  - 10.2991/wartia-16.2016.346
ID  - Liu2016/05
ER  -