Last years research gave some preliminary results in approaches to customer online purchase prediction. However, it still remains unclear what exact set of features of data instances should be incorporated in a model and is enough for prediction, what is the best data mining method (algorithm) to use, how stable over time could be such a model, whether a model is transferable from one online store to another. This study is focused on a heuristic approach to dealing with the problem under conditions of such theoretical and methodological diversity in order to find a quick and inexpensive first approximation to the solution or at least to find useful patterns and facts in the data.
In this paper the term of «intelligent data analysis» is discussed, the cloud computing concept is described. The system developed and deployed on the computer cluster by ECM department of BSUIR is shown as an example of the intelligent data analysis by means of cloud computing. Some results of research with the help of this system are given.
In paper the outcomes of mathematical modeling of statistical recognition of binary images are proposed. The offered hypothesis that the pixels constituting boundary of recognition object and an image background are secondary attributes at recognition is experimentally confirmed. As consequence, recognition reliability can be raised due to exception of these pixels at recognition. Basing on the offered example of training of models on the fixed training sample and taking into account that the prototype model is a special case of the rejecting model we have noted that recognition reliability of offered model cannot be lower than reliability of the prototype. The obtained results will be used in hardware realization of binary comparators.
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