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JMLR
2002
157views more  JMLR 2002»
13 years 6 months ago
Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions
This paper introduces the problem of combining multiple partitionings of a set of objects into a single consolidated clustering without accessing the features or algorithms that d...
Alexander Strehl, Joydeep Ghosh
INFFUS
2006
142views more  INFFUS 2006»
13 years 6 months ago
Moderate diversity for better cluster ensembles
Adjusted Rand index is used to measure diversity in cluster ensembles and a diversity measure is subsequently proposed. Although the measure was found to be related to the quality...
Stefan Todorov Hadjitodorov, Ludmila I. Kuncheva, ...
ATAL
2006
Springer
13 years 10 months ago
Efficient agent-based cluster ensembles
Numerous domains ranging from distributed data acquisition to knowledge reuse need to solve the cluster ensemble problem of combining multiple clusterings into a single unified cl...
Adrian K. Agogino, Kagan Tumer
GFKL
2004
Springer
117views Data Mining» more  GFKL 2004»
13 years 11 months ago
Cluster Ensembles
Cluster ensembles are collections of individual solutions to a given clustering problem which are useful or necessary to consider in a wide range of applications. The R package˜c...
Kurt Hornik
MCS
2005
Springer
13 years 12 months ago
Cluster-Based Cumulative Ensembles
Abstract. In this paper, we propose a cluster-based cumulative representation for cluster ensembles. Cluster labels are mapped to incrementally accumulated clusters, and a matching...
Hanan Ayad, Mohamed S. Kamel
MCS
2007
Springer
14 years 15 days ago
Selecting Diversifying Heuristics for Cluster Ensembles
Abstract. Cluster ensembles are deemed to be better than single clustering algorithms for discovering complex or noisy structures in data. Various heuristics for constructing such ...
Stefan Todorov Hadjitodorov, Ludmila I. Kuncheva
SDM
2009
SIAM
220views Data Mining» more  SDM 2009»
14 years 3 months ago
Bayesian Cluster Ensembles.
Cluster ensembles provide a framework for combining multiple base clusterings of a dataset to generate a stable and robust consensus clustering. There are important variants of th...
Hongjun Wang, Hanhuai Shan, Arindam Banerjee