In this article, we present FactoMineR an R package dedicated to multivariate data analysis. The main features of this package is the possibility to take into account different types of variables (quantitative or categorical), different types of structure on the data (a partition on the variables, a hierarchy on the variables, a partition on the individuals) and finally supplementary information (supplementary individuals and variables). Moreover, the dimensions issued from the different exploratory data analyses can be automatically described by quantitative and/or categorical variables. Numerous graphics are also available with various options. Finally, a graphical user interface is implemented within the Rcmdr environment in order to propose an user friendly package.

/pdf/factominer-an-r-package-for-multivariate-analysis-50jd8856rq.pdf

FactoMineR: An R Package for Multivariate Analysis

Principal component analysis PCA is a multivariate technique that analyzes a data table in which observations are described by several inter-correlated quantitative dependent variables. Its goal is to extract the important information from the table, to represent it as a set of new orthogonal variables called principal components, and to display the pattern of similarity of the observations and of the variables as points in maps. The quality of the PCA model can be evaluated using cross-validation techniques such as the bootstrap and the jackknife. PCA can be generalized as correspondence analysis CA in order to handle qualitative variables and as multiple factor analysis MFA in order to handle heterogeneous sets of variables. Mathematically, PCA depends upon the eigen-decomposition of positive semi-definite matrices and upon the singular value decomposition SVD of rectangular matrices. Copyright © 2010 John Wiley & Sons, Inc.

/pdf/principal-component-analysis-rvdwo0sk0j.pdf

Principal component analysis

Monographs on statistics and applied probability

Clustering is the partitioning of a set of objects into groups (clusters) so that objects within a group are more similar to each others than objects in different groups. Most of the clustering algorithms depend on some assumptions in order to define the subgroups present in a data set. As a consequence, the resulting clustering scheme requires some sort of evaluation as regards its validity.
The evaluation procedure has to tackle difficult problems such as the quality of clusters, the degree with which a clustering scheme fits a specific data set and the optimal number of clusters in a partitioning. In the literature, a wide variety of indices have been proposed to find the optimal number of clusters in a partitioning of a data set during the clustering process. However, for most of indices proposed in the literature, programs are unavailable to test these indices and compare them.
The R package NbClust has been developed for that purpose. It provides 30 indices which determine the number of clusters in a data set and it offers also the best clustering scheme from different results to the user. In addition, it provides a function to perform k-means and hierarchical clustering with different distance measures and aggregation methods. Any combination of validation indices and clustering methods can be requested in a single function call. This enables the user to simultaneously evaluate several clustering schemes while varying the number of clusters, to help determining the most appropriate number of clusters for the data set of interest.

NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set

This book considers why institutional forms of modern capitalist economies differ internationally, and proposes a typology of capitalism based on the theory of institutional complementarity Different economic models are not simply characterized by different institutional forms, but also by particular patterns of interaction between complementary institutions which are the core characteristics of these models Institutions are not just simply devices which would be chosen by 'social engineers' in order to perform a function as efficiently as possible; they are the outcome of a political economy process Therefore, institutional change should be envisaged not as a move towards a hypothetical 'one best way', but as a result of socio-political compromises Based on a theory of institutions and comparative capitalism, the book proposes an analysis of the diversity of modern economies - from America to Korea - and identifies five different models: the market-based Anglo-Saxon model; Asian capitalism; the Continental European model; the social democratic economies; and the Mediterranean model Each of these types of capitalism is characterized by specific institutional complementarities The question of the stability of the Continental European model of capitalism has been open since the beginning of the 1990s: inferior macroeconomic performance compared to Anglo-Saxon economies, alleged unsustainability of its welfare systems, too rigid markets, etc The book examines the institutional transformations that have taken place within Continental European economies and analyses the political project behind the attempts at transforming the Continental model It argues that Continental European economies will most likely stay very different from the market-based economies, and caat political strategies promoting institutional change aiming at convergence with the Anglo-Saxon model are bound to meet considerable opposition

The Diversity of Modern Capitalism

This paper presents a brief review of several endeavors to identify latent variables (axes or clusters). When dealing with textual data, these latent variables (clusters or axes) are sometimes designated ex ante by the term “topic”. The first attempts to identify interpretable latent variables dates back to factor analysis at the beginning of last century. Recent years have witnessed a series of algorithmic attempts such as non-negative matrix factorization (NMF) or latent Dirichlet allocation (LDA). In the meantime, latent variables are also identified through several hybridizations and synergies of principal axes methods and clustering techniques. A single medium-sized classical corpus (Shakespeare’s 154 Sonnets) will serve as a benchmark to sketch and compare in a compact way some characteristic features of several methods.

Looking for Topics : A Brief Review

The typologies and visualizations of the preceding chapter produce global panoramas of lexical tables. These panoramas can be generated whether or not the data are aggregated, whether or not they are natural partitions of a body of work (chapters, works of one author, articles, discourses, etc.), whether or not they result from artificial groupings (open-ended responses organized by categories, documents organized by subject matter, etc.).

Characteristic Textual Units, Modal Responses and Modal Texts

The study of texts using statistical methods constitutes a field of interest known as textual statistics. In recent years there have been important changes in the general context of this domain of research, as well as its objectives and the methodological principles it utilizes.

Textual Statistics Scope and Applications

Clustering techniques constitute a second family of data analysis techniques in addition to principal axes methods. These methods are used for representing proximities among the elements of a lexical table (rows or columns) through groupings or clusters.

Cluster Analysis of Words and Texts

Cet article propose une introduction au Data Mining. Celle-ci prend la forme d'une reflexion sur les interactions entre deux disciplines, Informatique et Statistique, collaborant a l'analyse de grands jeux de donnees dans une perspective d'aide a la decision. Sans pretention d'exhaustivite, differents exemples sont exposes issus du marketing bancaire, de la detection de defaillances dans un procede industriel ainsi que des problematiques aeronautiques pour l'aide au pilotage. Ils permettent de tirer quelques enseignements sur les pratiques du data mining : choix d'une methode, competences de l'utilisateur, fiabilite des resultats.

Ludovic Lebart

Papers

Looking for Topics : A Brief Review

Characteristic Textual Units, Modal Responses and Modal Texts

Textual Statistics Scope and Applications

Cluster Analysis of Words and Texts

Data mining et statistique. Discussion. Author's reply