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The statistical approach of the "text mining" consists in to transform a collection of text documents in a matrix of numeric values on which we can apply machine learning algorithms. The "unstructured document" designation is often used when one talks about text documents. This does not mean that he does not have a certain organization (titles, chapters, paragraphs, questions and answers, etc.). It shows first of all that we cannot express directly the collection in the form of a data table that is usually handled in data mining. To obtain this kind of data representation, a preprocessing phase is needed, then we extract relevant features to define the data table. These steps can influence heavily the relevance of the results. In this tutorial, I take an exercise that I lead with my students for my text mining course at the University. We perform all the analysis under R with the dedicated packages for text mining such as “XML” or “tm”. The issue here is to perform ...

The aim of image mining is to extract valuable knowledge from image data. In the context of supervised image classification, we want to assign automatically a label to image from their visual content. The whole process is identical to the standard data mining process. We learn a classifier from a set of classified images. Then, we can apply the classifier to a new image in order to predict its class membership. The particularity is that we must extract a vector of numerical features from the image before to launch the machine learning algorithm, and before to apply the classifier in the deployment phase. We deal with an image classification task in this tutorial. The goal is to detect automatically the images which contain a car. The main result is that, even if I have a basic knowledge about the image processing, I can lead the analysis with a facility which is symptomatic of the usability of Knime in this context. Keywords : image mining, image classification, image processing, featu...

In this tutorial, we show how to perform a dummy coding for categorical predictor variables in the context of the logistic regression learning process. In fact, this is an old tutorial that I was written a long time ago (2007), but it is not referenced in this blog (which was created in 2008). I found it in my archives because I plan to write soon a tutorial about the strategies for the selection of categorical variables in logistic regression. I was wondering if I had already written something that may be linked to this subject (the treatment of the categorical predictors in logistic regression) in the past. Obviously, I would have to check most often my archives. We use Tanagra 1.4.50 in this tutorial. Keywords : logistic regression, dummy coding, categorical predictor variables Components : SAMPLING, O_1_BINARIZE, BINARY LOGISTIC REGRESSION, TEST Tutorial : Dummy coding - Logistic Regression Dataset : heart-c.xlsx   References : Wikipedia, " Logistic Regression "

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 discretization transforms a continuous attribute into a discrete one. To do that, it partitions the range into a set of intervals by defining a set of cut points. Thus we must answer to two questions to lead this data transformation: (1) how to determine the right number of intervals; (2) how to compute the cut points. The resolution is not necessarily in that sequence. The best discretization is the one performed by an expert domain. Indeed, he takes into account other information than those only provided by the available dataset. Unfortunately, this kind of approach is not always feasible because: often, the domain knowledge is not available or it does not allow to determine the appropriate discretization; the process cannot be automated to handle a large number of attributes. So, we are often forced to found the determination of the best discretization on a numerical process. Discretization of continuous features as preprocessing for supervised learning process. First, we must d...

In this tutorial, we show that in certain circumstances, it is more convenient to use the factors computed from a principal component analysis (from the original attributes) as input features for the linear discriminant analysis algorithm. The new representation space maintains the proximity between the examples. The new features known as "factors" or "latent variables", which are a linear combination of the original descriptors, have several advantageous properties: (a) their interpretation very often allows to detect patterns in the initial space; (b) a very reduced number of factors allows to restore information contained in the data, we can moreover remove the noise from the dataset by using only the most relevant factors (it is a sort of regularization by smoothing the information provided by the dataset); (c) the new features form an orthogonal basis, learning algorithms such as linear discriminant analysis have a better behavior. This approach has a connectio...