CoFKM : a Centralized Method for Multiple-View Clustering

Guillaume Cleuziou (1) , Matthieu Exbrayat (1) , Lionel Martin (1) , Jacques-Henri Sublemontier (1)

1 LIFO - Laboratoire d'Informatique Fondamentale d'Orléans

This paper deals with clustering for multi-view data, i.e. objects described by several sets of variables or proximity matrices. The aim of this topic is to search for clustering patterns that perform a consensus between the patterns from different views. This requires to merge information from each view by performing a fusion process that identifies the agreement between the views. Various fusion strategies can be applied, occurring either before, after or during the clustering process. We draw our inspiration from the existing algorithms based on a centralized strategy. We propose a fuzzy clustering approach that generalizes the three fusion strategies and outperforms the main existing multi-view clustering algorithm both on synthetic and real datasets.

Keywords: machine learning; data mining; multi-view clustering

Domains: Machine Learning [cs.LG]

Guillaume Cleuziou, Matthieu Exbrayat, Lionel Martin, Jacques-Henri Sublemontier. CoFKM : a Centralized Method for Multiple-View Clustering. ICDM 2009, The Ninth IEEE International Conference on Data Mining, Dec 2009, Miami, United States. pp.752-757, ⟨10.1109/ICDM.2009.138⟩. ⟨hal-00460800⟩