Nicoleta Rogovschi

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This paper introduces a probabilistic self-organizing map for topographic clustering, analysis and visualization of multivariate binary data or categorical data using binary coding. 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(More)
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(More)
We propose a novel model based on the von Mises-Fisher (vMF) distribution for coclustering high dimensional sparse matrices. While existing vMF-based models are only suitable for clustering along one dimension, our model acts simultaneously on both dimensions of a data matrix. Thereby it has the advantage of exploiting the inherent duality between rows and(More)
Data mining allows the exploration of sequences of phenomena, whereas one usually tends to focus on isolated phenomena or on the relation between two phenomena. It offers invaluable tools for theoretical analyses and exploration of the structure of sentences, texts, dialogues, and speech. We report here the results of an attempt at using it for inspecting(More)
This paper proposes a spectral algorithm for cross-topographic clustering. It leads to a simultaneous clustering on the rows and columns of data matrix, as well as the projection of the clusters on a two-dimensional grid while preserving the topological order of the initial data. The proposed algorithm is based on a spectral decomposition of this data(More)
High spatial resolution satellite imagery has become an important source of information for geospatial applications. Automatic segmentation of high-resolution satellite imagery is useful for obtaining more timely and accurate information. In this paper we introduce a new approach for automatic image segmentation into different regions (corresponding to(More)
We explore in this paper a novel topological organization algorithm for data clustering and visualization named TPNMF. It leads to a clustering of the data, as well as the projection of the clusters on a two-dimensional grid while preserving the topological order of the initial data. The proposed algorithm is based on a NMF (Nonnegative Matrix(More)
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(More)