Name
Abstract | ||
|---|---|---|
When dealing with multiple clustering solutions, the problem of extrapolating a small number of good different solutions becomes crucial. This problem is faced by the so called Meta Clustering [12], that produces clusters of clustering solutions. Often such groups, called meta-clusters, represent alternative ways of grouping the original data. The next step is to construct a clustering which represents a chosen meta-cluster. In this work, starting from a population of solutions, we build meta-clusters by hierarchical agglomerative approach with respect to an entropy-based similarity measure. The selection of the threshold value is controlled by the user through interactive visualizations. When the meta-cluster is selected, the representative clustering is constructed following two different consensus approaches. The process is illustrated through a synthetic dataset. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-85567-5_91 | KES (3) |
Keywords | Field | DocType |
representative clustering,robust clustering,entropy-based similarity measure,intersection methods,clustering solution,alternative way,hierarchical agglomerative approach,meta clustering,good different solution,multiple clustering solution,different consensus approach,chosen meta-cluster,consensus clustering,interactive visualization | Data mining,Fuzzy clustering,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Computer science,Consensus clustering,Cluster analysis,Brown clustering,Single-linkage clustering | Conference |
Volume | ISSN | Citations |
5179 | 0302-9743 | 3 |
PageRank | References | Authors |
0.39 | 18 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ida Bifulco | 1 | 16 | 3.54 |
| Carmine Fedullo | 2 | 7 | 1.57 |
| Francesco Napolitano | 3 | 61 | 5.16 |
| Giancarlo Raiconi | 4 | 118 | 15.08 |
| Roberto Tagliaferri | 5 | 428 | 55.64 |