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niewiadomski methods for 2008

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The book is concentrated on the development and applications of soft computing methods to data processing based on natural language. Since the rise of electronic computational technologies, man has experienced some discomfort because it has been possible to communicate with a machine only by means of numbers and artificial command languages.

The book concerns the methods of intelligent data exploration known as linguistic summarization of databases. Summarization means here converting data from their numerical or mathematically expressed forms into linguistic forms, or, in a broader sense, into forms understandable for average users. Although such operations are illustrated nowadays mostly on examples and algorithms introduced and examined in this book do not depend on sources of information, nor the way transfer. We focus mostly on data meaning and structure, so not to limit the presented algorithms to network services only. For the same reasons, this volume places emphasis on exploring numerical and simple textual information, since it is the easiest in storage and, hence, the most typical kind of data. The technical details concerning the means of collecting and storing information are viewed in the context of relational databases, which does not mean that the methods discussed cannot be implemented for e.g. object-oriented databases or other techniques of storing data.

The presented material is a review of state-of-art knowledge in the domain of linguistic summarization of databases with a special emphasis on the author’s own contribution. In particular, the elements from the field of fuzzy sets and their extensions, Interval-Valued Fuzzy Sets and Type-2 Fuzzy Sets in the sense of Mendel, are included, and linguistic summarization methods based on these extensions, are developed. The concepts and algorithms typical of fuzzy set theory are not only mentioned, discussed and broadly illustrated with examples, but also enhanced and expressed by more universal means e.g. by interval-valued and type-2 membership functions. Information within this wide spectrum of knowledge is selected mostly due to two criteria: its possible practical use in linguistic summarization of data, and the necessity of providing sui generis completeness of the material.

 

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