Name
Abstract | ||
|---|---|---|
Clustering is an important research direction in data mining. However, there is no one clustering algo-rithm that can be applied efficiently in all situation. Clustering ensemble is the best way to solve the above-mentioned problems. It combines the results of multiple clustering algorithms, and the final result is significantly better than a single clustering algorithm. Although there is a lot of constraint information, the existing clustering ensemble algorithm does not utilize it. This paper uses constraint information in consensus function and proposes a Semi-supervised Selective Clustering Ensemble based on Chameleon (SSCEC) and Semi-supervised Selective Clustering Ensemble based on Ncut (SSCEN) to solve the above problem. SSCEC uses the chameleon algorithm as consensus function, and processes constraint informa-tion in subgraph partition and subgraph combining. SSCEN uses the Normalized cut algorithm as consen-sus function, and processes constraint information in the process of graph dichotomy. The experiment results show that our proposed two semi-supervised member selection clustering ensemble algorithms are better than other semi-supervised algorithms. (c) 2021 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.neucom.2021.07.056 | NEUROCOMPUTING |
Keywords | DocType | Volume |
Semi-supervised, Clustering ensemble, Member selection, Constraint information | Journal | 462 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tinghuai Ma | 1 | 107 | 11.50 |
| Zheng Zhang | 2 | 14 | 3.36 |
| Lei Guo | 3 | 0 | 0.34 |
| Xin Wang | 4 | 0 | 0.34 |
| Yurong Qian | 5 | 1 | 3.39 |
| Najla Al-Nabhan | 6 | 0 | 0.34 |