Name
Abstract | ||
|---|---|---|
Although there are a lot of clustering algorithms available in the literature, existing algorithms are usually afflicted by practical problems of one form or another, including parameter dependence and the inability to generate clusters of arbitrary shapes. In this paper we aim to solve these two problems by merging the merits of dominant sets and density based clustering algorithms. We firstly apply histogram equalization to eliminate the parameter dependence problem of the dominant sets algorithm. Noticing that the obtained clusters are usually smaller than the real ones, a density threshold based cluster growing step is then used to improve the clustering results, where the involved parameters are determined based on the initial clusters. This is followed by the second cluster growing step which makes use of the density relationship between neighboring data. Data clustering experiments and comparison with other algorithms validate the effectiveness of the proposed algorithm. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s11063-017-9767-3 | Neural Processing Letters |
Keywords | Field | DocType |
Cluster growing,Dominant sets,Parameter independence | Cluster (physics),Pattern recognition,Artificial intelligence,Merge (version control),Histogram equalization,Cluster analysis,Mathematics | Journal |
Volume | Issue | ISSN |
48 | 2 | 1370-4621 |
Citations | PageRank | References |
0 | 0.34 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian Hou | 1 | 126 | 17.11 |
| Xu E | 2 | 4 | 3.89 |
| Wei-Xue Liu | 3 | 26 | 3.81 |