Rough set based cluster ensemble selection

Ensemble clustering have been attracting lots of attentions, which combining several base data partitions to generate a single consensus partition with improved stability and robustness. Diversity is critical for the success of ensemble clustering. To enhance this characteristic, a subset of cluster...

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Bibliographische Detailangaben
Hauptverfasser:	Xueen Wang, Deqiang Han, Chongzhao Han
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	attribute significance Clustering algorithms Diversity reception ensemble clustering feature selection Glass Information entropy Lungs Partitioning algorithms rough set Set theory
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Beschreibung
Zusammenfassung:	Ensemble clustering have been attracting lots of attentions, which combining several base data partitions to generate a single consensus partition with improved stability and robustness. Diversity is critical for the success of ensemble clustering. To enhance this characteristic, a subset of cluster ensemble is selected by removing the redundant partitions. Combined with ranking and forward selection strategies, the significance of attribute defined in rough set theory is employed as a heuristic to find the subset of cluster ensemble. Experimental results on the UCI machine learning repository demonstrate that the proposed algorithm is feasible and effective.