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Displaying similar documents to “Canonical non-symmetrical correspondence analysis: an alternative in constrained ordination.”

Weighting quantitative and qualitative variables in clustering methods.

Karina Gibert, Ulises Cortés (1997)

Mathware and Soft Computing

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Description of individuals in ill-structured domains produces messy data matrices. In this context, automated classification requires the management of those kind of matrices. One of the features involved in clustering is the evaluation of distances between individuals. Then, a special function to calculate distances between individuals partially simultaneously described by qualitative and quantitative variables is required. In this paper properties and details of the metrics...

Local Principal Components Analysis.

Tomàs. Aluja Banet, Ramón Nonell Torrent (1991)

Qüestiió

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Principal Components Analysis deals mainly with the analysis of large data sets with multivariate structure in an observational context for exploraty purposes. The factorial planes produced will show the main oppositions between variables and individuals. However, we may be interested in going further by controlling the effect of some latent or third variable which expresses some well-defined phenomenon. We go through this by means of a graph among individuals, following the same idea...

On Solving the Maximum Betweenness Problem Using Genetic Algorithms

Savić, Aleksandar (2009)

Serdica Journal of Computing

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In this paper a genetic algorithm (GA) is applied on Maximum Betweennes Problem (MBP). The maximum of the objective function is obtained by finding a permutation which satisfies a maximal number of betweenness constraints. Every permutation considered is genetically coded with an integer representation. Standard operators are used in the GA. Instances in the experimental results are randomly generated. For smaller dimensions, optimal solutions of MBP are obtained by total enumeration. For...

Knowledge discovery in data using formal concept analysis and random projections

Cherukuri Aswani Kumar (2011)

International Journal of Applied Mathematics and Computer Science

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In this paper our objective is to propose a random projections based formal concept analysis for knowledge discovery in data. We demonstrate the implementation of the proposed method on two real world healthcare datasets. Formal Concept Analysis (FCA) is a mathematical framework that offers a conceptual knowledge representation through hierarchical conceptual structures called concept lattices. However, during the design of a concept lattice, complexity plays a major role.