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The aim of the correlation analysis is to characterize the existence, the nature and the strength of the relationship between two quantitative variables. The visual inspection of scatter plots is a prime instrument in a first step, when we have no idea about the form of the underlying relationship between the variables. But, in second step, we need statistical tools to measure the strength of the relationship and to assess its significance. In these slides, we present the Pearson's product-moment correlation. We show how to estimate its value using a sample. We present the inferential tools which enable to realize hypothesis testing and confidence interval estimation. But the Pearson correlation is appropriate only to characterize linear relationship. We study the possible solutions for problematic situations with, among others, the Spearman's rank correlation coefficient (Spearman's rho). Last, the partial correlation coefficient and the related inferential tools are descr...

The aim of clustering of categorical variables is to group variables according to their relationship. The variables in the same cluster are highly related; variables in different clusters are weakly related. In these slides, we describe an approach based on the Cramer’s V measure of association. We observe that the approach can highlight subset of variables which is useful - for instance - in a variable selection process for a subsequent supervised learning task. But, on the other hand, we have no indication about the nature of these associations. The interpretation of the groups is not obvious. This leads us to deepen the analysis and to take an interest in the clustering of the categories of nominal variables. An approach based on a measure of similarity between categories using the indicator variables (dummy variables) is described. Other approaches are also reviewed. The main advantage of this kind of analysis (clustering of categories) is that we can easily interpret the underlyin...

The discretization consists to transform a continuous attribute into a discrete (ordinal) attribute. The process determines a finite number of intervals from the available values, for which discrete numerical values are assigned. The two main issues of the process are: how to determine the number of intervals; how to determine the cut points. In this slides, we present some discretization methods for the unsupervised and supervised contexts. Keywords : discretization, data preprocessing, chi-merge, mdlpc, equal-frequency, equal-width, clustering, top-down, bottom-up, feature construction Components (Tanagra) : EQFREQ DISC, EQWIDTH DISC, MDLPC, BINARY BINNING, CONT TO DISC Slides : Discretization Tutorials : Tanagra Tutorials, " Discretization of continuous features ", may 2010.

The aim of clustering variables is to divide a set of numeric variables into disjoint clusters (subset of variables). In these slides, we present an approach based on the concept of latent component. A subset of variables is summarized by a latent component which is the first factor from the principal component analysis. This is a kind of "centroid" variable which maximizes the sum of the squared correlation with the existing variables. Various clustering algorithms based on this idea are described: a hierarchical agglomerative algorithm; a top down approach; and an approach which is inspired by the k-means method. Keywords : clustering, clustering variables, latent variable, latent component, clusters, groups, bottom-up, hierarchical agglomerative clustering, top down, varclus, k-means, pca, principal component analysis Components (Tanagra) : VARHCA, VARKMEANS, VARCLUS Slides : Clustering variables Tutorials : Tanagra tutorials, " Variable clustering (VARCLUS) ", 2...

Artificial neural networks are computational models inspired by an animal’s central nervous system (in particular brain) which is capable of machine learning as well as pattern recognition (Wikipedia). In these slides, we present the single layer and multilayer perceptrons, which are devoted to supervised learning process.  We describe the baseline of the approaches: the difference between the linear (single-layer) and non-linear (multilayer) classifiers; the representation power of the models; the learning algorithm (the Widrow-Hoff rule and the back propagation algorithm). Keywords : artificial neural network, perceptron, single layer, SLP, multilayer, MLP, widrow-hoff rule, backpropagation algorithm, linear classifier, non linear classifier Components (Tanagra) : MULTILAYER PERCEPTRON Slides : Single layer and multilayer perceptrons Tutorials :  Tanagra tutorials, " Configuration of a multilayer perceptron ", December 2017. Tanagra tutorials, " Multilayer perceptron -...

In the supervised learning context, the filter approach for feature selection consists in the selection of the most appropriate variables for any subsequent machine learning algorithm used for the construction of the model. The methods are mostly based on the correlation concept (in a large sense). They are interesting because they enable to handle quickly high-dimensional data sets. On the other hand, they are questionable because they do not take into account the characteristics of the model (e.g. linear, non-linear) that will be developed from the selected variables. Keywords : feature selection, filter methods, embedded methods, wrapper methods Components (Tanagra) : CFS FILTERING, FCBF FILTERING, MIFS FILTERING, MODTREE FILTERING, FEATURE RANKING, FISHER FILTERING, RUNS FILTERING, STEPDISC Slides : Filter methods Tutorials : Tanagra tutorials, " Filter methods for feature selection ", 2010. Tanagra tutorials, " Filter methods for feature selection (continuation) ...

Association rule learning is a popular approach to extract rules from large databases. Initially intended to transactional data, especially for the market basket analysis, the method can be applied to any binary or binarized data. In these slides, we show the outline of the approach. We present a basic algorithm to generate association rules from data. We highlight the influence of the settings (minimum support and minimum confidence) for the reduction of the search space, and thus for the reduction  of the amount of calculations. Keywords : association rule, association rules, itemset, frequent itemset, eclat algorithm, support, confidence, lift Components (Tanagra) : A PRIORI, A PRIORI MR, A PRIORI PT, FREQUENT ITEMSETS, SPV ASSOC RULE, SPV ASSOC TREE Slides : Association rule learning References : Wikipedia, " Association Rule Learning ". M. Zaki, S. Parthasaraty, M. Ogihara, W. Li, “ New Algorithms for Fast Discovery of Association Rules ”, in Proc. of KDD’97, p. 283-296,...