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We present in this paper a new clustering method which provides self-organization of hierarchical clustering. This method represents large datasets on a forest of original trees which are projected on a simple 2D geometric relationship using tree map representation. The obtained partition is represented by a map of tree maps, which define a tree of data. In this paper, we provide the rules that build...
We introduce a new learning approach, which provides simultaneously self-organizing map (SOM) and local weight vector for each cluster. The proposed approach is computationally simple, and learns a different features vector weights for each cell (relevance vector). Based on the self-organizing map approach, we present two new simultaneously clustering and weighting algorithms: local weighting observation...
This paper deals with the problem of combining multiple clustering algorithms using the same data set to get a single consensus clustering. Our contribution is to formally define the cluster consensus problem as an optimization problem. to reach this goal, we propose an original existing algorithm but still relatively unknown method named relational analysis (RA). This method has several advantages...
This paper introduces a new probabilistic topological map as generative model that includes mixture of Gaussian and Bernoulli distribution. This model is dedicated to cluster mixed data with continuous and categorical variables. This model is fitted by maximum likelihood using the EM algorithm. Examples using real data set allow to validate our model. The proposed approach has the advantage comparing...
This paper introduces a probabilistic self-organizing map for clustering, analysis and visualization of multivariate binary data. We propose a probabilistic formalism dedicated to binary data in which cells are represented by a Bernoulli distribution. Each cell is characterized by a prototype with the same binary coding as used in the data space and the probability of being different from this prototype...
In this paper, we present a new heuristic measure for optimizing database used as input layer of Self Organizing Map (SOM). This heuristic called Hl-SOM (Heuristic Input for SOM) consists of selection of variables for clustering with SOM algorithm. HI-SOM allows to identify and to select important variables in the feature spaces. Thus, we eliminate redundant variables and those do not contain enough...
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