Displaying similar documents to “Correspondence analysis and two-way clustering.”

Profile analysis of mothers susceptible to contaminant exposure in the Algarve region: Application of the HJ-BIPLOT method

A. Serafim, R. Company, B. Lopes, N. Silva, E. Castela, M.J. Bebianno, G. Castela (2012)

Biometrical Letters

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The HJ-BIPLOT method developed by Galindo (1986) was applied in order to identify and categorize mothers vulnerable to environmental contamination in the Algarve region (South Portugal). The application of the BIPLOT method made it possible to recognize the most important exposure routes for contamination, showing that workplace, diet and smoking habits seem the most significant factors contributing to maternal and foetal exposure vulnerability

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

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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...

Detecting a data set structure through the use of nonlinear projections search and optimization

Victor L. Brailovsky, Michael Har-Even (1998)

Kybernetika

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Detecting a cluster structure is considered. This means solving either the problem of discovering a natural decomposition of data points into groups (clusters) or the problem of detecting clouds of data points of a specific form. In this paper both these problems are considered. To discover a cluster structure of a specific arrangement or a cloud of data of a specific form a class of nonlinear projections is introduced. Fitness functions that estimate to what extent a given subset of...

An alternative methodology for imputing missing data in trials with genotype-by-environment interaction: some new aspects

Sergio Arciniegas-Alarcón, Marisol García-Peña, Wojtek Janusz Krzanowski, Carlos Tadeu dos Santos Dias (2014)

Biometrical Letters

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A common problem in multi-environment trials arises when some genotypeby- environment combinations are missing. In Arciniegas-Alarcón et al. (2010) we outlined a method of data imputation to estimate the missing values, the computational algorithm for which was a mixture of regression and lower-rank approximation of a matrix based on its singular value decomposition (SVD). In the present paper we provide two extensions to this methodology, by including weights chosen by cross-validation...

Notes on the bias of dissimilarity indices for incomplete data sets: the case of archaelogical classification.

Angela Montanari, Stefania Mignani (1994)

Qüestiió

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The problem of missing data is particularly present in archaeological research where, because of the fragmentariness of the finds, only a part of the characteristics of the whole object can be observed. The performance of various dissimilarity indices differently weighting missing values is studied on archaeological data via a simulation. An alternative solution consisting in randomly substituting missing values with character sets is also examined. Gower's dissimilarity coefficient...

Data mining techniques using decision tree model in materialised projection and selection view.

Y. W. Teh (2004)

Mathware and Soft Computing

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With the availability of very large data storage today, redundant data structures are no longer a big issue. However, an intelligent way of managing materialised projection and selection views that can lead to fast access of data is the central issue dealt with in this paper. A set of implementation steps for the data warehouse administrators or decision makers to improve the response time of queries is also defined. The study concludes that both attributes and tuples, are important...