Name
Abstract | ||
|---|---|---|
We propose a novel method, called heterogeneous clustering ensemble (HCE), to generate robust clustering results that combine multiple partitions (clusters) derived from various clustering algorithms. The proposed method combines partitions of various clustering algorithms by means of newly-proposed the selection and the crossover operation of the genetic algorithm (GA) during the evolutionary process. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11893318_45 | Discovery Science |
Keywords | Field | DocType |
heterogeneous clustering ensemble,evolutionary process,novel method,genetic algorithm,novel framework,multiple partition,various clustering algorithm,robust cluster result,robust clustering result,crossover operation | Data mining,Fuzzy clustering,Canopy clustering algorithm,Clustering high-dimensional data,CURE data clustering algorithm,Correlation clustering,Computer science,Artificial intelligence,FLAME clustering,Cluster analysis,Machine learning,Single-linkage clustering | Conference |
Volume | ISSN | ISBN |
4265 | 0302-9743 | 3-540-46491-3 |
Citations | PageRank | References |
7 | 0.57 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hye-Sung Yoon | 1 | 34 | 2.11 |
| Sang-Ho Lee | 2 | 77 | 7.76 |
| Sung-bum Cho | 3 | 63 | 3.02 |
| Ju Han Kim | 4 | 248 | 30.80 |