Nicoleta Rogovschi

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This paper introduces a probabilistic self-organizing map for topographic clustering, analysis and visu-alization 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(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)
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)
This paper describes a new algorithm to learn Self-Organizing map as Markov Mixture Models. Our model realizes an unsupervised learning using unlabelled evolutionary data sets, namely those that describe sequential data. The new formalism that we present is valid for all structure of graphical models. We use E-M (Expectation-Maximisation) standard algorithm(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)
We propose a novel model based on the von Mises-Fisher (vMF) distribution for co-clustering 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 du-ality between rows(More)
Opinion Mining is the field of computational study of peopel's emotional behavior expressed in text. The purpose of this article is to introduce a new framework for emotion (opinion) mining based on topological unsupervised learning and hierarchical clustering. In contrast to supervised learning, the problem of clustering characterization in the context of(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)