Analyse Multivariée Archives - Datanovia https://www.datanovia.com/en/fr/etiquette-produit/analyse-multivariee/ Exploration de Données et Statistiques pour l'Aide à la Décision Thu, 04 Jan 2024 13:13:10 +0000 fr-FR hourly 1 https://wordpress.org/?v=6.8.2 Practical Guide To Principal Component Methods in R https://www.datanovia.com/en/fr/produit/practical-guide-to-principal-component-methods-in-r/ https://www.datanovia.com/en/fr/produit/practical-guide-to-principal-component-methods-in-r/#comments Fri, 21 Sep 2018 20:15:37 +0000 https://www.datanovia.com/en/?post_type=product&p=7178 This book provides a solid practical guidance to summarize, visualize and interpret the most important information in a large multivariate data sets, using principal component methods in R.

You will learn:

  • Principal Component Analysis (PCA) for summarizing a large dataset of continuous variables
  • Simple Correspondence Analysis (CA) for large contingency tables formed by two categorical variables
  • Multiple Correspondence Analysis (MCA) for a data set with more than 2 categorical variables
  • Methods for analyzing a data set containing a mix of variables (continuous and categorical) structured or not into groups: Factor Analysis of Mixed Data (FAMD) and Multiple Factor Analysis (MFA).
  • Hierarchical Clustering on Principal Components (HCPC), which is useful for performing clustering with a data set containing only categorical variables or with a mixed data of categorical and continuous variables
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]]> https://www.datanovia.com/en/fr/produit/practical-guide-to-principal-component-methods-in-r/feed/ 32 Practical Guide to Cluster Analysis in R https://www.datanovia.com/en/fr/produit/practical-guide-to-cluster-analysis-in-r/ https://www.datanovia.com/en/fr/produit/practical-guide-to-cluster-analysis-in-r/#comments Sat, 15 Sep 2018 17:44:36 +0000 https://www.datanovia.com/en/?post_type=product&p=7164 This book provides practical guide to cluster analysis, elegant visualization and interpretation. It covers 1) dissimilarity measures; 2) partitioning clustering methods (K-means, K-Medoids and CLARA algorithms); 3) hierarchical clustering method; 4) clustering validation and evaluation strategies; 5) advanced clustering methods, including: Hierarchical k-means clustering, Fuzzy clustering, Model-based clustering and Density-based clustering.

 

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Or, Buy and Download Now a PDF Copy by clicking on the "ADD TO CART" button down below. You will receive a link to download a PDF copy (click to see the book preview)

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