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R ( http://www.r-project.org/ ) is one of the most exciting free data mining software projects of these last years. Its popularity is absolutely justified (see Kdnuggets Polls - Data Mining/ Analytic Tools Used - 2011 ). Among the reasons which explain this success, we distinguish two very interesting characteristics: (1) we can extend almost indefinitely the features of the tool with the packages; (2) we have a programming language which allows to perform easily sequences of complex operations. But this second property can be also a drawback. Indeed, some users do not want to learn a new programming language before being able to realize projects. For this reason, tools which allow to define the sequence of commands with diagrams (such as Tanagra, Knime, RapidMiner, etc.) still remain a valuable alternative with the data miners. I n this tutorial, we present the "Rattle" package which allows to the data miners to use R without needing to know the associated programming langua

Model deployment is the last task of the data mining steps. It corresponds to several aspects e.g. generating a report about the data exploration process, highlighting the useful results; applying models within an organization's decision making process; etc . In this tutorial, we look at the context of predictive data mining. We are concerned about the construction of the model from a labeled dataset; the storage of the model; the distribution of the model, without the dataset used for its construction; the application of the model on new instances in order to assign them a class label from their description (the values of the descriptors). We describe the filehash package for R which allows to deploy a model easily. The main advantage of this solution is that R can be launched under various operating systems. Thus, we can create a model with R under Windows; and apply the model in another environment, for instance with R under Linux. The solution can be easily generalized on a la

Few people know it. In fact, several tools are installed when we launch the SETUP file of SIPINA (setup_stat_package.exe). This is the case of REGRESS which is intended to multiple linear regression. Even if a multiple linear regression procedure is incorporated to Tanagra, REGRESS can be useful essentially because it is very easy to use. It has the advantage of being very easy to handle while being consistent with a degree course in Econometrics. As such, it may be useful for anyone wishing to learn about the regression without too much get involved in the learning of a new software. Keywords : regress, econometrics, multiple linear regression, outliers, influential points, normality tests, residuals, Jarque-Bera test, normal probability plot, sipina.xla, add-in Tutorial : en_sipina_regress.pdf Dataset : ventes-regression.xls References : R. Rakotomalala, " Econométrie - Régression Linéaire Simple et Multiple ". D. Garson, " Multiple regression ".

Comparing the behavior of tools is always a good way to improve them. To check and validate the implementation of methods . The validation of the implemented algorithms is an essential point for data mining tools. Even if two programmers use the same references (books, articles), the programming choice can modify the behavior of the approach (behaviors according to the interpretation of the convergence conditions for instance). The analysis of the source code is possible solution. But, if it is often available for free software, this is not the case for commercial tools. Thus, the only way to check them is to compare the results provided by the tools on a benchmark dataset . If there are divergences, we must explain them by analyzing the formulas used. To improve the presentation of results . There are certain standards to observe in the production of reports, consensus initiated by reference books and / or leader tools in the field. Some ratios should be presented in a certain way. Us

CART (Breiman and al., 1984) is a very popular classification tree (says also decision tree) learning algorithm. Rightly. CART incorporates all the ingredients of a good learning control: the post-pruning process enables to make the trade-off between the bias and the variance; the cost complexity mechanism enables to "smooth" the exploration of the space of solutions; we can control the preference for simplicity with the standard error rule (SE-rule); etc. Thus, the data miner can adjust the settings according to the goal of the study and the data characteristics. The Breiman's algorithm is provided under different designations in the free data mining tools. Tanagra uses the "C-RT" name. R, through a specific package , provides the "rpart" function. In this tutorial, we describe these implementations of the CART approach according to the original book (Breiman and al., 1984; chapters 3, 10 and 11). The main difference between them is the implementation