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This tutorial complements the course material concerning the Kohonen map or Self-organizing map ( June 2017 ). In a first time, we try to highlight two important aspects of the approach: its ability to summarize the available information in a two-dimensional space; Its combination with a cluster analysis method for associating the topological representation (and the reading that one can do) to the interpretation of the groups obtained from the clustering algorithm. We use the R software and the “Kohonen” package (Wehrens et Buydens, 2007). In a second time, we carry out a comparative study of the quality of the partitioning with the one obtained with the K-means algorithm. We use an external evaluation i.e. we compare the clustering results with pre-established classes. This procedure is often used in research to evaluate the performance of clustering methods. It takes on its meaning when it is applied to artificial data where the true class membership is known. We use the K-Means and ...
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...
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