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[85730] Artykuł: A knowledge discovery from full-text document collections using clustering and interpretable genetic-fuzzy systemsCzasopismo: Proc. of the 11th edition of International Conference on Multimedia & Network Information Systems, Wroclaw, Poland, September 12-14, 2018. Advances in Intelligent Systems and Computing, Springer, Cham. Tom: 833, Strony: 434-443ISSN: 2194-5365 ISBN: 978-3-319-98678-4 Wydawca: SPRINGER INTERNATIONAL PUBLISHING AG, GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND Opublikowano: 2019 Seria wydawnicza: Advances in Intelligent Systems and Computing Autorzy / Redaktorzy / Twórcy Grupa MNiSW: Materiały z konferencji międzynarodowej (zarejestrowane w Web of Science) Punkty MNiSW: 0 Klasyfikacja Web of Science: Proceedings Paper DOI Web of Science Keywords: clustering  fuzzy rule-based systems  text document  knowledge discovery  information retrieval  human language understanding  |
The paper presents a concept of a hybrid system consisting of two our original techniques from the computational intelligence area and its application to knowledge discovery from full-text document collection. Our first technique - self-organizing neural network with one dimensional neighborhood and dynamically evolving topological structure - aims at automatically determining the number of groups in the document collection and at grouping the documents in terms of their similarity. In turn, the main goal of our second approach - multi-objective evolutionary designing technique of fuzzy rule-based classifiers with optimized accuracy-interpretability trade-off - is to extract the most important keywords from documents and to generate classification rules which can be helpful in understanding and isolating the subjects of documents collected in the founded groups. The proposed concept may also be useful to develop systems operating in a wide area of human language understanding problems.