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- Title
An exemplar-based clustering using efficient variational message passing.
- Authors
Ibrahim, Mohamed Hamza; Missaoui, Rokia
- Abstract
Clustering is a crucial step in scientific data analysis and engineering systems. Thus, an efficient cluster analysis method often remains a key challenge. In this paper, we introduce a general purpose exemplar-based clustering method called (MEGA), which performs a novel message-passing strategy based on variational expectation–maximization and generalized arc-consistency techniques. Unlike message passing clustering methods, MEGA formulates the message-passing schema as E- and M-steps of variational expectation–maximization based on a reparameterized factor graph. It also exploits an adaptive variant of generalized arc consistency technique to perform a variational mean-field approximation in E-step to minimize a Kullback–Leibler divergence on the model evidence. Dissimilar to density-based clustering methods, MEGA has no sensitivity to initial parameters. In contrast to partition-based clustering methods, MEGA does not require pre-specifying the number of clusters. We focus on the binary-variable factor graph to model the clustering problem but MEGA is applicable to other graphical models in general. Our experiments on real-world problems demonstrate the efficiency of MEGA over existing prominent clustering algorithms such as Affinity propagation, Agglomerative, DBSCAN, K-means, and EM.
- Subjects
CLUSTER analysis (Statistics); MESSAGE passing (Computer science); ENGINEERING mathematics; SYSTEMS engineering; DATA analysis; ALGORITHMS
- Publication
Data Mining & Knowledge Discovery, 2021, Vol 35, Issue 1, p248
- ISSN
1384-5810
- Publication type
Article
- DOI
10.1007/s10618-020-00720-w