Titlebook: Data Mining and Computational Intelligence; Abraham Kandel,Mark Last,Horst Bunke Book 2001 Physica-Verlag Heidelberg 2001 computational in

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Dale S. Huff MD,Dale S. Huff MDapplication areas, however, data is more complicated: real-life data is often obtained as an image from a camera rather than a few measurements. Furthermore, this image can also change dynamically. In this paper, we present several examples of how soft computing is related to mining such data.

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Dale S. Huff MD,Dale S. Huff MDotos, a large amount of the information stored on the Web is in the form of images, etc. It is therefore desirable to use this image information in data mining. Unfortunately, most existing data mining techniques have been designed for mining numerical data and are thus not well suited for image dat

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https://doi.org/10.1007/978-1-4614-0019-6eory as well as natural selection, and hence is called “genetic modeling”. In order to find a predictive model from the nonlinear time series, we make use of ‘survival of the fittest’ principle of evolution. Through the process of genetic evolution, the AIC criteria are used as the performance measu

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Studies in Fuzziness and Soft Computinghttp://image.papertrans.cn/d/image/262926.jpg

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Psa617

Maria Pia De Padova,Antonella Tostithe ensuing information granules. We propose two fundamental concepts in data mining: associations and rules. Associations are direction-free constructs that capture the most essential components of the overall structure in database. The relevance of associations is expressed by the cardinality of t

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Hectic

Maria Pia De Padova,Antonella Tosti, rule search based on rule test and rating, on- and offline rule reduction and finally rule base analysis and optimization. With respect to the broad spectrum of applications, there are different methods available for each of these steps. An overview is given in the first part of this paper, with e

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J. Lakatos,K. Köllő,G. Skaliczki,G. Holnapyd FARD (Fuzzy Association Rule Discovery), for mining fuzzy association rules. FARD is based on the pruning of the fuzzy concept lattice, and can be applied equally to classical or fuzzy databases, by scanning the database only once.