Research on Density Sensitive Clustering Algorithm for Non-convex Sets

DOI

10.2991/icmeit-19.2019.129How to use a DOI?

Keywords

mean shift, spectral clustering, density sensitivity, ensemble selection.

Abstract

Applying Clustering to non-convex data is a challenging task, and traditional clustering algorithms often fail to achieve good results. In this paper, an improved spectral clustering algorithm based on density sensitivity (DSISC algorithm) is proposed. By using the ensemble selection strategy for the mean shift algorithm, relatively good optional clusters are selected from the non-convex data sets, and then the number of clusters is transported into the spectral clustering algorithm as input, and the density-sensitive distance is used as the similarity measure. The experimental results give us clear information that the DSISC is better than traditional mean shift algorithm and spectral clustering algorithms in normalized mutual information clustering error rate.

Copyright

© 2019, 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  - Liwen Song
AU  - Jiahui Qi
AU  - Min Wu
PY  - 2019/04
DA  - 2019/04
TI  - Research on Density Sensitive Clustering Algorithm for  Non-convex Sets
BT  - Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)
PB  - Atlantis Press
SP  - 804
EP  - 811
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-19.2019.129
DO  - 10.2991/icmeit-19.2019.129
ID  - Song2019/04
ER  -