sutton001

The aim of this tutorial is to show the programming of the famous "word count" algorithm from a set of files stored in HDFS file system. The "word count" is a state-of-the-art example for the programming under Hadoop. It is described everywhere on the web. But, unfortunately, the tutorials which describe the task are often not reproducible. The dataset are not available. The whole process, including the installation of the Hadoop framework, are not described. We do not know how to access to the files stored in the HDFS file system. In short, we cannot run programs and understand in details how they work. In this tutorial, we describe the whole process. We detail first the installation of a virtual machine which contains a single-node Hadoop cluster. Then we show how to install R and RStudio Server which allows us to write and run a program. Last, we write some programs based on the mapreduce scheme. The steps, and therefore the source of errors, are numerous. We wil...

Big Data is a very popular topic these last years. The big data analytics refers to the process to discovering useful information or knowledge from big data. That is an important issue for organizations. In concrete terms, the aim is to extend, adapt or even create novel exploratory data analysis or data mining approaches to new data sources of which the main characteristics are “volume”, “variety” and “velocity”. Distributed computing is essential in the big data context. It is illusory to want infinitely increase the power of servers for following the exponential growth of information to process. The solution depends on the efficient cooperation of a myriad of networked computers, ensuring both the volume management and computing power. Hadoop is a solution commonly cited for this requirement. This is a set of algorithms (an open-source software framework written in Java) for distributed storage and distributed processing of very large data sets (Big Data) on computer clusters built ...

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 -...