Multivariate Data
EFFECTIVENESS PREDICTION OF MEMORY BASED CLASSIFIERS FOR THE CLASSIFICATION OF MULTIVARIATE DATA SET
... Classification is a step by step practice for allocating a given piece of input into any of the given category. Classification is an essential Machine Learning technique. There are many classification problem occurs in ...
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CHEMOSTAT: EXPLORATORY MULTIVARIATE DATA ANALYSIS SOFTWARE.
... EXPLORATORY MULTIVARIATE DATA ANALYSIS ...exploratory data analysis software application for academic use that is easy to install and can be handled without user-level programming due to extensive ...
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Breast cancer metabolomics: from analytical platforms to multivariate data analysis. A review
... the data distribution is normal (parametric) or not normal ...the data is parametric are T-tests [31,47,48,50,53,57,59,62,68,70,71,73,74,76–79,84] and ANOVA [33,44,46,63,64,66,68,69,72,83] T-tests, such as ...
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Detection of Outliers in Multivariate Data: A Method Based on Influence Eigen
... Abstract—Outliers can be defined simply as an observation (or a subset of observations) that is isolated from the other observations in the data set. There are two main reasons that motivate people to find ...
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A COMPARATIVE STUDY ON MICROAGGREGATION TECHNIQUES FOR MICRODATA PROTECTION
... of data. Various microaggregation techniques: fixed-size and data-oriented for univariate and multivariate data exists in the ...disclosing data and data utility was ...optimal ...
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Rev. bras. farmacogn. vol.20 número1
... for multivariate data analysis using principal component analysis (PCA) and hierarchical cluster analysis (HCA) ...pyrolysis data from samples of different brands was able to represent ...
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José do Patrocinio Hora Alves1 , Lucas Cruz Fonseca2 , Raisa de Siqueira Alves Chielle
... using multivariate data analysis, such as principal component analysis (PCA) and hierarchical cluster analysis ...a data matrix consisting of 12 sampling stations and mean concentrations of 23 water ...
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Multivariate analysis of marketing data - applications for bricolage market
... of multivariate data analysis were used. The multivariate methods can be classified into methods of analyzing dependencies, characterized in that one or more variables are considered dependencies and ...
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Exploratory data analysis and clustering of multivariate spatial hydrogeological data by means of GEO3DSOM, a variant of Kohonen’s Self-Organizing Map
... exploratory data analysis and clustering of multivariate data ...artificial data set and a hydro- chemical data set. The hydrochemical data set consists of 131 groundwater ...
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Phenotypic plasticity of southern ocean diatoms: key to success in the sea ice habitat?
... and multivariate data analysis and modelling are used to measure the macromolecular composition of individual Antarctic diatom cells and quantitatively assess the degree of phenotypic plasticity of cell ...
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Quantifying Postural Control during Exergaming Using Multivariate Whole-Body Movement Data: A Self-Organizing Maps Approach.
... position data of the nine body segments, describing the postures adopted during gameplay in the frontal plane, were resampled at 30 Hz using cubic spline interpolation to account for intrinsic sample frequency ...
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Surveillance of arthropod vector-borne infectious diseases using remote sensing techniques: a review.
... meteorological data in the Middle East has been modeled ...Meteorological data were collected on the ground from 114 weather stations in nine countries, including Saudi Arabia, Kuwait, Iran, and ...
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Ciênc. saúde coletiva vol.21 número12
... Its data was collected in 30 consecutive days from September to November 2014 in 12-hour shifts chosen by probabilistic draw in urgent and emergency care services within the Unified Health ...
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A Simple Approach to Integrate the Ecotoxicological and Chemical Data for the Establishment of Environmental Risk Levels
... The quality of the sediments from SES has been studied already, with the purpose of environmental diagnosing and monitoring (Abessa, 2002; Abessa et al., 1998; 2001; 2005; Lamparelli et al., 2001; Prósperi, 2002; Rachid, ...
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COSIMA data analysis using multivariate techniques
... mensional data set into a reduced dimension which still re- tains the most important features of the original data but is small enough to be numerically ...our data set multiple times, we effectively ...
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Regional-scale identification of groundwater-surface water interaction using hydrochemistry and multivariate statistical methods, Wairarapa Valley, New Zealand
... whether data from single sam- ples collected at different sites years apart could be meaning- fully ...insufficient data were available to conduct the Mann-Kendall ...site data ar- ray derived as ...
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Comparison Between Multivariate Methods Applied for the Evaluation of Genetic Divergence in Cacao (Theobroma cacao L.)
... four multivariate statistics presented close association only when considered in pairs: in this case the Mahalanobis' (D 2 ) with the Euclidean distance obtained from canonical variates (dcv), and mean Euclidean ...
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A Survey Paper on Crime Prediction Technique Using Data Mining
... Crime is classically unpredictable. It is not necessarily random, neither does it take place persistently in space or time. A Good theoretical understanding is needed to provide practical crime prevention solutions that ...
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Journal of Multivariate Analysis
... When it is not possible to obtain the analytical null distribution of a test statistic U, Monte Carlo hypothesis tests can be used to perform the test. Monte Carlo tests are commonly used in a wide variety of ...
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Rev. CEFAC vol.15 número4
... Neck, data were collected and submitted to bivariate and multivariate analysis to demonstrate the involvement of variables in the quality of life of institutions of tertiary care for treatment of ...After ...
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