Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)

Research on Density Sensitive Clustering Algorithm for Non-convex Sets

Authors
Liwen Song, Jiahui Qi, Min Wu
Corresponding Author
Liwen Song
Available Online April 2019.
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Proceedings
Part of series
Advances in Computer Science Research
Publication Date
April 2019
ISBN
978-94-6252-708-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/icmeit-19.2019.129How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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
PB  - Atlantis Press
SP  - 804
EP  - 811
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-19.2019.129
DO  - https://doi.org/10.2991/icmeit-19.2019.129
ID  - Song2019/04
ER  -