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Classification croisée et modèles

Y. Bencheikh (2010)

RAIRO - Operations Research

The relations between automatic clustering methods and inferentiel statistical models have mostely been studied when the data involves only one set. We propose to study these relations in the case of data involving two sets. We shall look at cross clustering methods as suggested by Govaert [6]; we show that these methods, like the simple clustering methods, can be considered as a clustering approach of a mixture model. We introduce the notion of crossed mixture from a concret example and...

Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution

Ewa Świercz (2010)

International Journal of Applied Mathematics and Computer Science

A new supervised classification algorithm of a heavily distorted pattern (shape) obtained from noisy observations of nonstationary signals is proposed in the paper. Based on the Gabor transform of 1-D non-stationary signals, 2-D shapes of signals are formulated and the classification formula is developed using the pattern matching idea, which is the simplest case of a pattern recognition task. In the pattern matching problem, where a set of known patterns creates predefined classes, classification...

Classification into two von Mises distributions with unknown mean directions

Kryštof Eben (1983)

Aplikace matematiky

The paper deals with two Mises distributions on the circle with unknown mean directions and a common concentration parameter that is known. The likelihood rule and the plug-in rule are examined. For the statistic of the plug-in rule, the moment generating function is given and a method of obtaining the moments is proposed.

Classification of Images Background Subtraction in Image Segmentation

Francesco Mola, Jaromír Antoch, Luca Frigau, Claudio Conversano (2016)

Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica

Many image segmentation algorithms have been proposed to partition an image into foreground regions of interest and background regions to be ignored. These algorithms use pixel intensities to partition the image, so it should be good practice to choose an appropriate background color as different as possible from the foreground one. In the case of a unique digitizing operation the user can make the choice of background color by himself in order to obtain a good result in the segmentation process,...

Classification of Smoking Cessation Status Using Various Data Mining Methods

Kartelj, Aleksandar (2010)

Mathematica Balkanica New Series

AMS Subj. Classification: 62P10, 62H30, 68T01This study examines different approaches of binary classification applied to the prob- lem of making distinction between former and current smokers. Prediction is based on data collected in national survey performed by the National center for health statistics of America in 2000. The process consists of two essential parts. The first one determines which attributes are relevant to smokers status, by using methods like basic genetic algorithm and different evaluation...

Classifiers for doubly multivariate data

Mirosław Krzyśko, Michał Skorzybut, Waldemar Wołyński (2011)

Discussiones Mathematicae Probability and Statistics

This paper proposes new classifiers under the assumption of multivariate normality for multivariate repeated measures data (doubly multivariate data) with Kronecker product covariance structures. These classifiers are especially useful when the number of observations is not large enough to estimate the covariance matrices, and thus the traditional classifiers fail. The quality of these new classifiers is examined on some real data. Computational schemes for maximum likelihood estimates of required...

Clustering of Symbolic Data based on Affinity Coefficient: Application to a Real Data Set

Áurea Sousa, Helena Bacelar-Nicolau, Fernando C. Nicolau, Osvaldo Silva (2013)

Biometrical Letters

In this paper, we illustrate an application of Ascendant Hierarchical Cluster Analysis (AHCA) to complex data taken from the literature (interval data), based on the standardized weighted generalized affinity coefficient, by the method of Wald and Wolfowitz. The probabilistic aggregation criteria used belong to a parametric family of methods under the probabilistic approach of AHCA, named VL methodology. Finally, we compare the results achieved using our approach with those obtained by other authors....

Clustering of vaguely defined objects

Libor Žák (2003)

Archivum Mathematicum

This paper is concerned with the clustering of objects whose properties cannot be described by exact data. These can only be described by fuzzy sets or by linguistic values of previously defined linguistic variables. To cluster these objects we use a generalization of classic clustering methods in which instead of similarity (dissimilarity) of objects, used fuzzy similarity (fuzzy dissimilarity) to define the clustering of fuzzy objects.

Coalescence floue fondée sur des ℛ -regroupements maximaux

Abdelwaheb Rebai (1992)

Mathématiques et Sciences Humaines

Les concepts d'éléments R-ressemblants à un prototype X et de R-regroupement d'objets introduits dans cet article, sont basés sur la notion de relation de S-comparaison R définie au moyen d'un indice scalaire de similarité défini entre sous-ensembles flous. Cette relation tient compte du fait que la similarité et la non-dissimilarité des sous-ensembles flous ne sont pas en général des synonymes. Une technique de coalescence floue basée sur des R-regroupements maximaux est également introduite.

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