Name
Abstract | ||
|---|---|---|
•A new algorithm is proposed to address challenges of clustering large data sets.•UNIC has near linear time and space complexity and does not require control parameters to be tuned in advance.•The algorithm derives cluster structure assessing distances between selected arbitrary points and the rest of the set employing methods from robust statistics.•Experimental results on synthetic and real world data show comparable performance of the algorithm with the selected clustering methods as well as a good ability to scale. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.patcog.2019.107117 | Pattern Recognition |
Keywords | DocType | Volume |
Cluster analysis,Hard (conventional,crisp) clustering,Nonparametric algorithms,Data mining,Big data | Journal | 100 |
Issue | ISSN | Citations |
1 | 0031-3203 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nadiia Leopold | 1 | 0 | 0.34 |
| Oliver Rose | 2 | 17 | 10.43 |