Name
Abstract | ||
|---|---|---|
Previous clustering ensemble algorithms usually use a consensus function to obtain a final partition from the outputs of the initial clustering. In this paper, we propose a new clustering ensemble method, which generates a new feature space from initial clustering outputs. Multiple runs of an initial clustering algorithm like k-means generate a new feature space, which is significantly better than pure or normalized feature space. Therefore, running a simple clustering algorithm on generated feature space can obtain the final partition significantly better than pure data. In this method, we use a modification of k-means for initial clustering runs named as "Intelligent k-means", which is especially defined for clustering ensembles. The results of the proposed method are presented using both simple k-means and intelligent kmeans. Fast convergence and appropriate behavior are the most interesting points of the proposed method. Experimental results on real data sets show effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-77226-2_41 | IDEAL |
Keywords | Field | DocType |
simple clustering algorithm,final partition,new clustering ensemble method,initial clustering output,new feature space,new efficient approach,initial clustering algorithm,previous clustering ensemble,clustering ensemble,initial clustering,k means,feature space | Canopy clustering algorithm,Fuzzy clustering,CURE data clustering algorithm,Data stream clustering,Pattern recognition,Correlation clustering,Computer science,Artificial intelligence,Constrained clustering,Cluster analysis,Machine learning,Single-linkage clustering | Conference |
Volume | ISSN | ISBN |
4881 | 0302-9743 | 3-540-77225-1 |
Citations | PageRank | References |
9 | 0.55 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Azimi, Javad | 1 | 153 | 10.65 |
| Monireh Abdoos | 2 | 62 | 5.28 |
| Morteza Analoui | 3 | 124 | 24.94 |