Weighted topological cultering (modular, hybrid and collaborative approaches)

Cette thèse est consacrée d'une part, à l'étude d'approches de caractérisation des classes découvertes pendant l'apprentissage non-supervisé, et d'autre part, à la classification non-supervisée modulaire, hybride et collaborative. L'étude se focalise essentiellement sur deux axes : - la caractérisation des classes en utilisant la pondération et la sélection des variables pertinentes, ainsi que l'utilisation de la notion de mémoire pendant le processus d'apprentissage topologique non-supervisé; - l'utilisation de plusieurs techniques de clustering en parallèle et en série : approches modulaires, hybrides et collaboratives. Nous nous intéressons plus particulièrement dans cette thèse aux cartes auto-organisatrices de Kohonen qui constituent une technique bien adaptée à la classification non-supervisée permettant une visualisation des résultats sous forme d'une carte topographique. Nous proposons plusieurs techniques de pondérations de l'apprentissage de ces cartes ainsi qu'une nouvelle stratégie de compétition permettant de garder en mémoire l'historique de l'apprentissage. En utilisant un test statistique pour la sélection des variables pertinentes pondérées, nous répondons au problème de la réduction des dimensions, ainsi qu'au problème de la caractérisation des classes découvertes. Concernant le deuxième axe, nous utilisons le formalisme mathématique de l'analyse relationnelle (AR) pour combiner plusieurs résultats de classification. Enfin, nous proposons une nouvelle approche conçue pour faire collaborer plusieurs classifications topographiques entre elles ,en préservant la confidentialité des données.This thesis is focused, on the one hand, to study clustering anlaysis approaches in an unsupervised topological learning, and in other hand, to the topological modular, hybrid and collaborative clustering. This study is adressed mainly on two problems: - cluster characterization using weighting and selection of relevant variables, and the use of the memory concept during the learning unsupervised topological process; - and the problem of the ensemble clustering techniques : the modularization, the hybridization and collaboration. We are particularly interested in this thesis in Kohonen's self-organizing maps which have been widely used for unsupervised classification and visualization of multidimensional datasets. We offer several weighting approaches and a new strategy which consists in the introduction of a memory process into the competition phase by calculating a voting matrix at each learning iteration. Using a statistical test for selecting relevant variables, we will respond to the problem of dimensionality reduction, and to the problem of the cluster characterization. For the second problem, we use the relational analysis approach (RA) to combine multiple topological clustering results.PARIS13-BU Sciences (930792102) / SudocSudocFranceF

Vertical collaborative clustering using generative topographic maps

International audienceCollaborative clustering is a recent field of Machine Learning that shows similarities with both transfer learning and ensemble learning. It uses two-step approaches where different clustering algorithms first process data individually and then exchange their information and results with a goal of mutual improvement. In this article, we introduce a new collaborative learning approach based on collaborative clustering principles and applied to the Generative Topographic Mapping (GTM) algorithm. Our method consists in applying the GTM algorithm on different data sets where similar clusters can be found (same feature spaces and similar data distributions), and then to use a collaborative framework on the generated maps with the goal of transferring knowledge between them. The proposed approach has been validated on several data sets, and the experimental results have shown very promising performances

Topological multi-view clustering for collaborative filtering

International audienc

Joint Multi-View Collaborative Clustering

International audienceData is increasingly being collected from multiple sources and described by multiple views. These multi-view data provide richer information than traditional single-view data. Fusing the former for specific tasks is an essential component of multi-view clustering. Since the goal of multi-view clustering algorithms is to discover the common latent structure shared by multiple views, the majority of proposed solutions overlook the advantages of incorporating knowledge derived from horizontal collaboration between multi-view data and the final consensus. To fill this gap, we propose the Joint Multi-View Collaborative Clustering (JMVCC) solution, which involves the generation of basic partitions using Non-negative Matrix Factorization (NMF) and the horizontal collaboration principle, followed by the fusion of these local partitions using ensemble clustering. Furthermore, we propose a weighting method to reduce the risk of negative collaboration (i.e., views with low quality) during the generation and fusion of local partitions. The experimental results, which were obtained using a variety of data sets, demonstrate that JMVCC outperforms other multi-view clustering algorithms and is robust to noisy views

Compose: A Domain Specific Language for scientific code computation

International audienc

Community detection in Attributed Network

International audienc