Clustering the Constitutive Elements of Measuring Tables Data Structure

Rebbouh, Amar

doi:10.1080/03610910600716597

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…(ii) If the matrix has the same dimension ( , ), in [6], an algorithm of -means type is proposed based on the Hilbert-Schmidt inner product to classify these matrix objects. If does not have the same dimension, we can envisage a step of completion in order to obtain a structure of juxtaposition of data tables of the same dimension.…”

Section: Classical Approachmentioning

confidence: 99%

“…Finally, the third type of reduction leads to new uncorrelated variables but poses significant mathematical problems such as the search for compromise space and the number of observations to be used for the reduction of each entry table (see [4,5]). If the number of observations of each variable is the same for each object, the input data can be considered as a structure of data matrices (see [6]). …”

Section: Introductionmentioning

confidence: 99%

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Classification of the Entities Represented by Samples from Gaussian Distribution

Rebbouh

2016

Advances in Decision Sciences

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This paper aims to cluster entities which are described by a data matrix. Under the assumption of normality of observations contained in each table, each entity is represented by samples from Gaussian distribution, that is, a number of measurements in the data matrix, the sample mean vector, and the sample covariance. We propose a new distance based on Mahalanobis's discriminant score to measure the similarity between objects. The present study is thought to be an important and interesting topic of research not only in the quest for an adequate model of the data representation but also in the choice of the distance index between entities that would allow justifying the homogeneity of any observed classes.

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Section: Classical Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of the Entities Represented by Samples from Gaussian Distribution

Rebbouh

2016

Advances in Decision Sciences

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Clustering Objects Described by Juxtaposition of Binary Data Tables

Rebbouh

2008

Journal of Applied Mathematics and Decision Sciences

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This paper seeks to develop an allocation of 0/1 data matrices to physical systems upon a Kullback-Leibler distance between probability distributions. The distributions are estimated from the contents of the data matrices. We discuss an ascending hierarchical classification method, a numerical example and mention an application with survey data concerning the level of development of the departments of a given territory of a country.

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Clustering the Constitutive Elements of Measuring Tables Data Structure

Cited by 2 publications

References 6 publications

Classification of the Entities Represented by Samples from Gaussian Distribution

Classification of the Entities Represented by Samples from Gaussian Distribution

Clustering Objects Described by Juxtaposition of Binary Data Tables

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