Proceedings of the 1998 conference on Advances in neural information processing systems II
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Finding Consistent Clusters in Data Partitions
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Statistical Models for Co-occurrence Data
Statistical Models for Co-occurrence Data
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Solving cluster ensemble problems by bipartite graph partitioning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pairwise probabilistic clustering using evidence accumulation
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
On the Scalability of Evidence Accumulation Clustering
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
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Evidence accumulation clustering (EAC) is a clustering combination method in which a pair-wise similarity matrix (the so-called co-association matrix) is learnt from a clustering ensemble. This coassociation matrix counts the co-occurrences (in the same cluster) of pairs of objects, thus avoiding the cluster correspondence problem faced by many other clustering combination approaches. Starting from the observation that co-occurrences are a special type of dyads, we propose to model co-association using a generative aspect model for dyadic data. Under the proposed model, the extraction of a consensus clustering corresponds to solving a maximum likelihood estimation problem, which we address using the expectation-maximization algorithm. We refer to the resulting method as probabilistic ensemble clustering algorithm (PEnCA). Moreover, the fact that the problem is placed in a probabilistic framework allows using model selection criteria to automatically choose the number of clusters. To compare our method with other combination techniques (also based on probabilistic modeling of the clustering ensemble problem), we performed experiments with synthetic and real benchmark data-sets, showing that the proposed approach leads to competitive results.