Name
Abstract | ||
|---|---|---|
This paper presents a consensus function based on a new formulation for the median partition problem to address the problem of ensemble clustering. It is based on the underlying idea of minimizing the distance between pairs of objects identified as the most dissimilar among the set of all available objects. By initially finding a pairing of objects and minimizing specifically such dissimilarities a more robust heuristic is achieved to solve the problem of finding a median object, especially in cases where the objects variability is accentuated. The performance of this method is assessed in relation to other well known ensemble clustering methods. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/SCCC.2012.38 | SCCC |
Keywords | Field | DocType |
ensemble clustering,robust heuristic,new formulation,median partition problem,new consensus function,ensemble clustering problem,available object,underlying idea,objects variability,median object,statistical analysis | k-medians clustering,Fuzzy clustering,Pattern recognition,Correlation clustering,Computer science,Consensus clustering,Constrained clustering,FLAME clustering,Artificial intelligence,Cluster analysis,Machine learning,Single-linkage clustering | Conference |
ISSN | Citations | PageRank |
1522-4902 | 0 | 0.34 |
References | Authors | |
14 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Duarte Abdala | 1 | 16 | 2.73 |
| Xiaoyi Jiang | 2 | 2184 | 206.38 |