ACM Computing Surveys (CSUR)
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Cluster validation techniques for genome expression data
Signal Processing - Special issue: Genomic signal processing
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Cluster ensemble and its applications in gene expression analysis
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
An overview of clustering methods
Intelligent Data Analysis
Weighted Cluster Ensemble Using a Kernel Consensus Function
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Weighted cluster ensembles: Methods and analysis
ACM Transactions on Knowledge Discovery from Data (TKDD)
Statistical Analysis and Data Mining
A scalable framework for cluster ensembles
Pattern Recognition
Projective Clustering Ensembles
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
A time-efficient pattern reduction algorithm for k-means clustering
Information Sciences: an International Journal
Data clustering by minimizing disconnectivity
Information Sciences: an International Journal
Measuring Similarity between Sets of Overlapping Clusters
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Minimum spanning tree based split-and-merge: A hierarchical clustering method
Information Sciences: an International Journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering local frequency items in multiple databases
Information Sciences: an International Journal
On possibilistic clustering with repulsion constraints for imprecise data
Information Sciences: an International Journal
Uncovering overlapping cluster structures via stochastic competitive learning
Information Sciences: an International Journal
Information Sciences: an International Journal
CoBAn: A context based model for data leakage prevention
Information Sciences: an International Journal
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In this paper we give a general definition for the concept 'optimal clustering' which is applicable to overlapping clusterings. Overlapping clusterings are a generalization of hard clusterings and their structure is formally developed in this paper. It is generally assumed that the domain of clustering is too heuristic to develop a general, i.e. axiomatic, definition for an optimal clustering. It is shown, however, that such a definition can be given within the domain of overlapping clusterings, using the new concept of dual clustering developed in this paper. A second concept that underlies our definition of optimal clustering is the average clustering, also playing an important role in the domain of cluster ensembles. Using the general concepts discussed in this paper, it is then shown that under some conditions it is assured that the final hard clustering extracted by majority vote from a given set of clusterings, is optimal over all hard clusterings. Unlike traditional research related to validating clusterings, we do not develop a new cluster validation measure on top of the many existing ones, but rather we develop a general framework for cluster validation measures, at least within the domain of overlapping clusterings. This framework allows to develop some general theorems about clustering.