Paper Info
Title
A MAP Approach to Evidence Accumulation Clustering.
Abstract
The Evidence Accumulation Clustering (EAC) paradigm is a clustering ensemble method which derives a consensus partition from a collection of base clusterings obtained using different algorithms. It collects from the partitions in the ensemble a set of pairwise observations about the co-occurrence of objects in a same cluster and it uses these co-occurrence statistics to derive a similarity matrix, referred to as co-association matrix. The Probabilistic Evidence Accumulation for Clustering Ensembles (PEACE) algorithm is a principled approach for the extraction of a consensus clustering from the observations encoded in the co-association matrix based on a probabilistic model for the co-association matrix parameterized by the unknown assignments of objects to clusters. In this paper we extend the PEACE algorithm by deriving a consensus solution according to a MAP approach with Dirichlet priors defined for the unknown probabilistic cluster assignments. In particular, we study the positive regularization effect of Dirichlet priors on the final consensus solution with both synthetic and real benchmark data.
Year
DOI
Venue
2013
10.1007/978-3-319-12610-4_6
PATTERN RECOGNITION APPLICATIONS AND METHODS, ICPRAM 2013
Keywords
Field
DocType
Clustering algorithm,Clustering ensembles,Probabilistic modeling,Evidence accumulation clustering,Prior knowledge
Hierarchical clustering,Canopy clustering algorithm,CURE data clustering algorithm,Pattern recognition,Correlation clustering,Computer science,Consensus clustering,FLAME clustering,Artificial intelligence,Biclustering,Cluster analysis,Machine learning
Conference
Volume
ISSN
Citations 
318
2194-5357
0
PageRank 
References 
Authors
0.34
16
6
Name
Order
Citations
PageRank
André Lourenço131245.33
Samuel Rota Bulò256433.69
Nicola Rebagliati3343.30
Ana L. N. Fred41317195.30
Mário A. T. Figueiredo57203561.50
Marcello Pelillo61888150.33