Combining clustering solutions with varying number of clusters
نویسندگان
چکیده
Cluster ensemble algorithms have been used in different field like data mining, bioinformatics and pattern recognition. Many of them use label correspondence as a step which can be performed with some accuracy if all the input partitions are generated with same k. Thus these algorithms produce good results if this k is close to the actual number of clusters in the dataset. This puts great restriction if user has no idea of the number of clusters. In this paper we show through experimental studies that good ensembles can be generated even if the input solutions contain different number of clusters. Keywords—Clustering, Cluster Ensemble
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