Research on Density Sensitive Clustering Algorithm for Non-convex Sets
Liwen Song, Jiahui Qi, Min Wu
Available Online April 2019.
- https://doi.org/10.2991/icmeit-19.2019.129How to use a DOI?
- mean shift, spectral clustering, density sensitivity, ensemble selection.
- 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.
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 -