Data Mining with IBM SPSS Modeler (IBM SPSS Clementine)
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Produs:Nou, Ofer garanție, Cu factură
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Numar articol:187736430
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Disponibilitate:Indisponibil
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Preț:146,00 Lei
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Anuntul a expirat la:09.07.2019, 18:13
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Specificatii
This book presents the most common techniques used in data mining
in a simple and easy to understand through one of the most common
software solutions from among those existing in the market, in
particular, IBM SPSS CLEMENTINE whose current name is IBM SPSS
MODELER. Pursued as initial aim clarifying the applications
concerning methods traditionally rated as difficult or dull. It
seeks to present applications in data mining without having to
manage high mathematical developments or complicated theoretical
algorithms, which is the most common reason for the difficulties in
understanding and implementation of this matter. Today data mining
is used in different fields of science. Noteworthy applications in
banking, and financial analysis of markets and trade, insurance and
private health, in education, in industrial processes, in medicine,
biology and bioengineering, telecommunications and in many other
areas. Essentials to get started in data mining, regardless of the
field in which it is applied, is the understanding of own concepts,
task that does not require nor much less the domain of scientific
apparatus involved in the matter. Later, when either necessary
operative advanced, computer programs allow the results without
having to decipher the mathematical development of the algorithms
that are under the procedures. This book describes the simplest
possible data mining concepts, so that they are understandable by
readers with different training. The chapters begin describing the
techniques in affordable language and then presenting the way to
treat them through practical applications. An important part of
each chapter are case studies completely resolved, including the
interpretation of the results, which is precisely the most
important thing in any matter with which they work. The book begins
with an introduction to mining data and its phases. In successive
chapters develop the initial phases (selection of information, data
exploration, data cleansing, transformation of data, etc.).
Subsequently elaborates on specific data mining, both predictive
and descriptive techniques. Predictive techniques covers all models
of regression, discriminant analysis, decision trees, neural
networks and other techniques based on models. The descriptive
techniques vary dimension reduction techniques, techniques of
classification and segmentation (clustering), and exploratory data
analysis techniques.
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