Jacques-Henri Sublemontier

CoFKM: A Centralized Method for Multiple-View Clustering

This paper deals with clustering for multi-view data, i.e. objects described by several sets of variables or proximity matrices. Many important domains or applications such as Information Retrieval, biology, chemistry and marketing are concerned by this problematic. The aim of this data mining research field 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 and solves the conflicts. 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.

Integrating Pairwise Constraints into Clustering Algorithms: Optimization-Based Approaches

In this paper we introduce new models for semi-supervised clustering problem, in particular we address this problem from the representation space point of view. Given a dataset enhanced with constraints (typically must-link and cannot-link constraints) and any clustering algorithm, the proposed approach aims at learning a projection space for the dataset that satisfies not only the constraints but also the required objective of the clustering algorithm on unenhanced data. We propose a boosting framework to weight the constraints and infers successive projection spaces in such a way that algorithm performance is improved. We experiment this approach on standard UCI datasets and show the effectiveness of our algorithm.

Unsupervised collaborative boosting of clustering: An unifying framework for multi-view clustering, multiple consensus clusterings and alternative clustering

In this paper, we propose a collaborative framework that is able to solve multi-view and alternative clustering problems using some clustering ensemble and semi-supervised clustering principles. We provide a mechanism to control, via an information sharing model, different clustering algorithms to obtain consensus or alternative clustering solutions. The strong point is that our approach does not need to know which clustering algorithms to use nor their parameters.