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In a previous paper , we described a multithreading strategy for the linear discriminant analysis . The aim was to take advantage of the multicore processors of the recent computers. We noted that for the same memory occupation than the standard implementation, we can decrease dramatically the computation time according to the dataset characteristics. The solution however had two drawbacks: the number of cores used was dependent on the number of classes K of the dataset; the load of the cores depended on classes’ distributions. For instance, for one of dataset with K = 2 highly unbalanced classes, the gain was negligible compared to the single-threaded version. In this paper, we present a new approach for the multithreaded implementation of the linear discriminant analysis, available in Sipina 3.11. It allows to overcome the two bottlenecks of the previous version. The capacity of the machine is fully used. More interesting, the number of used threads (cores) becomes customizable, allo...

Some enhancements regarding factor analysis approaches (PCA - principal component analysis, MCA - multiple correspondence analysis, CA - correspondence analysis, FDMA - factorial analysis of mixed data) have been incorporated. In particular, outputs have been completed. The VARIMAX rotation has been improved. Thanks to Frédéric Glausinger for optimized source code. The Benzecri correction is added in the MCA outputs. Thanks to Bernard Choffat for this suggestion. Download page : setup

A new multithreaded version of linear discriminant analysis is added to Sipina 3.11. Compared to the previous, it presents two assets: (1) it is able to use all the resources available on the machines with multi-core processors or multiprocessor; (2) the load balancing is better . It requires however more amount of memory, the internal structures of calculation are duplicated M times (M is the number of threads). A tutorial will come to compare the behavior of this approach with the previous version and the single-threaded implementation. Keywords : linear discriminant analysis, multihreaded implementation, multithread Sipina website : Sipina Download : Setup file

Most of the modern personal computers have multicore CPU. This increases considerably their processing capabilities. Unfortunately, the popular free data mining tools does not really incorporate the multithreaded processing in the data mining algorithms they provide, aside from particular case such as ensemble methods or cross-validation process. The main reason of this scarcity is that it is impossible to define a generic framework whatever the mining method. We must study carefully the sequential algorithm, detect the opportunity of multithreading, and reorganize the calculations. We deal with several constraints: we must not increase excessively the memory occupation, we must use all the available cores, and we must balance the loads on the threads. Of course, the solution must be simple and operational on the usual personal computers. Previously, we implemented a solution for the decision tree induction in Sipina 3.5. We studied also the solutions incorporated in Knime and RapidMin...

The linear discriminant analysis has been enhanced. All operations are performed in a single pass on the data. A multithreaded version of the linear discriminant analysis has been added. It improves the execution speed by distributing calculations on any hearts (computer with a multi-core processor) or processors (multi-processor computer) available on the computer. A tutorial will describe the behavior of these new implementations on some large databases. Keywords : linear discriminant analysis, multihreaded implementation Sipina website : Sipina Download : Setup file

Usually, as a factor analysis approach, we use the principal component analysis (PCA) when the active variables are quantitative; the multiple correspondence analysis (MCA) when they are all categorical. But what to do when we have a mix of these two types of variables? A possible strategy is to discretize the quantitative variables and use the MCA. But this procedure is not recommended if we have a small dataset (a few number of instances), or if the number of qualitative variables is low in comparison with the number of quantitative ones. In addition, the discretization implies a loss of information. The choice of the number of intervals and the calculation of the cut points are not obvious. Another possible strategy is to replace each qualitative variable by a set of dummy variables (a 0/1 indicator for each category of the variable to recode). Then we use the PCA. This strategy has a drawback. Indeed, because the dispersions of the variables (the quantitative variables and the indi...

The correspondence analysis (or factorial correspondence analysis) is an exploratory technique which enables to detect the salient associations in a two-way contingency table. It proposes an attractive graphical display where the rows and the columns of the table are depicted as points. Thus, we can visually identify the similarities and the differences between the rows profiles (between the columns profiles). We can also detect the associations between rows and columns. The correspondence analysis (CA) can be viewed as an approach to decompose the chi-squared statistic associated with a two-way contingency table into orthogonal factors. In fact, because CA is a descriptive technique, it can be applied to tables even if the chi-square test of independence is not appropriate. The only restriction is that the table must contain positive or zero values, the calculating the sum of the rows and the columns is possible, the rows and columns profiles can be interpreted. The correspondence ana...