Abstract
The paper focuses on a practical use of knowledge extraction mechanisms in mining databases. The fuzzy-based methods that enable the linguistic interpretation of large sets of numerical data, are presented. In particular, generating the so-called linguistic summaries of databases, exemplified by About half of records have very high values of attribute A, in sense of Yager [1] with further improvements [2], [3] is described.
The original contribution by the author is the class of algorithms, based on linguistic summaries, which enable automated generating of brief textual news or comments to be published in press and/or WWW. The obtained messages describe quantitative dependencies among chosen values or attributes. Moreover, the produced results are expressed in semi-natural language which makes them readable for an average user. Finally, a prototype implementation on sample data is described.
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Niewiadomski, A. (2006). News Generating Via Fuzzy Summarization of Databases. In: Wiedermann, J., Tel, G., Pokorný, J., Bieliková, M., Štuller, J. (eds) SOFSEM 2006: Theory and Practice of Computer Science. SOFSEM 2006. Lecture Notes in Computer Science, vol 3831. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11611257_40
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DOI: https://doi.org/10.1007/11611257_40
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