Diversifying Heuristics for Cluster Ensembles
نویسندگان
چکیده
Cluster ensembles are deemed to be better than single clustering algorithms for discovering complex or noisy structures in data. We consider different heuristics to introduce diversity in cluster ensembles and study their individual and combined effect on the ensemble accuracy. Our experiments with three artificial and three real data sets, and 12 ensemble types, showed that the most successful diversifying heuristic was the random choice of the number of clusters for each ensemble member.
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