Fuzzy logic and neurofuzzy applications explained
Fuzzy logic and neurofuzzy applications explained
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
Fast sequential and parallel algorithms for association rule mining: a comparison
Fast sequential and parallel algorithms for association rule mining: a comparison
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PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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The VLDB Journal — The International Journal on Very Large Data Bases
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UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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Journal of Computer and System Sciences
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ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
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PAKDD'09 Proceedings of the 13th Pacific-Asia international conference on Knowledge discovery and data mining: new frontiers in applied data mining
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The following are the aims of the paper: (1) To call the attention of the community of Discovery Science (DS) to certain existing formal systems for DS developed in Prague in the 1960s through the 1980s suitable for DS and unfortunately largely unknown. (2) To illustrate the use of the calculi in question by the example of the GUHA method of hypothesis generation by computer, subjecting this method to a critical evaluation in the context of contemporary data mining. (3) To stress the importance of fuzzy logic for DS and to present the state of mathematical foundations of fuzzy logic. (4) Finally, to present a running research program of developing calculi of symbolic fuzzy logic for DS and for a fuzzy GUHA method.