An exemplar-based clustering using efficient variational message passing

• Ibrahim, Mohamed Hamza ^[1] ; Missaoui, Rokia ^[1]
  1. [1] Université du Québec en Outaouais

     Université du Québec en Outaouais

     CA.10.07.81017, Canadá

     
• Localización: Data mining and knowledge discovery, ISSN 1384-5810, Vol. 35, Nº 1, 2021, págs. 248-289
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • 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.