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Interval-valued linguistic summaries of databases

Niewiadomski A., Ochelska J., Szczepaniak P. S.

Control and Cybernetics

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2006

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Vol. 35, no 2

415-443

EN

The so-called linguistic summaries of databases are the semi-natural language sentences that enable distilling the most relevant information from large numbers of tuples, and present it in the human consistent forms. Recently, the methods of constructing and evaluating linguistic summaries have been based on Zadeh's fuzzy sets, which represent uncertain data. The main aim of the paper is to enhance and generalize the Yager's approach to linguistic summarization of data. This enhancement is based on interval-valued fuzzy sets. The newly presented methods enable handling fuzzy concepts, whose membership degrees are not given by real values explicitly, but are approximated by intervals in [0,1]. From now on, the Yager's approach can be viewed as a special case of the method presented in this paper. Finally, illustrative examples are presented.

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Two Measures of Words Similarity - Textual Fuzzy Similarity Measure and Sequence Kernels

Ochelska J.

Journal of Applied Computer Science

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2005

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Vol. 13, nr 2

71-78

EN

In this paper two novel methods capable of operating at the word level are presented. The first method is the textual fuzzy similarity measure [4,5,6], and the other one - the word-sequence kernels [1,2,3]. Both methods can operate on characters, although this property is of minor importance for the effective processing of textual documents. The methods are briefly described, and their common features as well as differences between them are pointed out. An outline of the application area closes the presentation of the two approaches.

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Linguistic summaries applied to medical textual databases

Ochelska J., Niewiadomski A., Szczepaniak P. S.

Journal of Medical Informatics & Technologies

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2001

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Vol. 2, Part. 1

MI125--MI130

EN

The paper focuses on textual semi-structured data processing and mining. An original composition of a simple method of textual data mining [1] and Yager's linguistic summaries of databased [2] proposed in the paper makes it possible to summarize not only numerical or string data, but – even and especially – the textual noncrisp and semi-structured information as well. As a result of application and implementation of the presented method to a real medical database a user-friendly and easy-to-operate system is achieved.