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Improvements have been introduced, a new component is added. HAC . Hierarchical agglomerative clustering. Computing time has been dramatically improved. We will detail the new procedure used in a new tutorial soon. CATVARHAC . Classification of the levels of the nominal variables. The calculations are based on the work of Abdallah and Saporta (1998). The component performs an agglomerative hierarchical clustering of the levels of qualitative variables. The Dice's index is used as the distance measures. Several kind of linkage criteria are proposed: single linkage, complete linkage, average linkage, Ward's method. A tutorial will come to describe the method soon. Download page : setup

Personal computers become more and more efficient. They are mostly equipped with multi-core processors. At the same time, most of the data mining tools, free or not, are often based on single-threaded calculations. Only one core is used during calculations, while others remain inactive. Previously, we have introduced two multithreaded variants of linear discriminant analysis in Sipina 3.10 and 3.11 . During the analysis that allowed me to develop the solutions introduced in Sipina, I had much studied parallelization mechanisms available in other Data Mining Tools. They are rather scarce. I noted that highly sophisticated strategies are proposed for the R software. These are often environments that enable to develop programs for multi-core processors machines, multiprocessor machines, and even for computer cluster. I studied in particular the "parallel" package which is itself derived from 'snow' and 'multicore' packages. Let us be quite clear. The library can...

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