Active Sampling for Constrained Clustering
作者： Masayuki Okabe , , Seiji Yamada ,
DOI: 10.20965/JACIII.2014.P0232
关键词: CURE data clustering algorithm 、 Canopy clustering algorithm 、 Single-linkage clustering 、 Constrained clustering 、 Cluster analysis 、 FLAME clustering 、 Consensus clustering 、 Correlation clustering 、 Computer science 、 Data mining
摘要: Constrained Clustering is a framework of improving clustering performance by using supervised information, which generally set constraints about data pairs. Since constrained depends on to use, we need method select good that are expected promote performance. In this paper, propose such method, actively pairs be variance iteration. This consists bagging based cluster ensemble algorithm integrates clusters produced k-means with random ordered assignment. Experimental results show our outperforms sampling method.
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