Name
Abstract | ||
|---|---|---|
Cluster ensemble offers an effective approach for aggregating multiple clustering results in order to improve the overall clustering robustness and stability. It also helps improve accuracy by combing clustering results from component methods that utilise different parameters (e.g., number of clusters), avoiding the need for carefully pre-setting the values of such parameters in a single clustering process. Since founded, many topics regarding cluster ensemble have been proposed and promising results gained. These include the generation of ensemble members and consensus of ensemble members. In this paper, link-based consensus methods for the ensemble of fuzzy c-means are proposed. Different from traditional clustering techniques, the clusters which are generated by fuzzy c-means are fuzzy sets. The proposed methods therefore employ a fuzzy graph to represent the relationships between component clusters upon which to derive the final ensemble clustering results. Using various benchmark datasets, the proposed methods are tested against typical traditional methods. The experimental results demonstrate that the proposed fuzzy-link-based clustering ensemble approach generally outperforms the others in terms of accuracy. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/FUZZ-IEEE.2014.6891806 | FUZZ-IEEE |
Keywords | Field | DocType |
component clusters,fuzzy set theory,pattern clustering,ensemble member generation,fuzzy-link-based clustering ensemble approach,benchmark datasets,matrix algebra,link-based pairwise similarity matrix approach,fuzzy graph,clustering stability,fuzzy sets,single clustering process,graph theory,fuzzy c-means clustering ensemble,link-based consensus methods,clustering robustness,benchmark testing,clustering algorithms,robustness,accuracy | Data mining,Fuzzy clustering,Computer science,Consensus clustering,FLAME clustering,Artificial intelligence,Cluster analysis,Ensemble learning,Single-linkage clustering,Correlation clustering,Pattern recognition,Constrained clustering,Machine learning | Conference |
ISSN | Citations | PageRank |
1544-5615 | 1 | 0.36 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pan Su | 1 | 82 | 11.72 |
| Changjing Shang | 2 | 212 | 34.92 |
| Qiang Shen | 3 | 864 | 55.09 |