×

The GUHA method and its meaning for data mining. (English) Zbl 1186.68160

Summary: The paper presents the history and present state of the GUHA method, its theoretical foundations and its relation and meaning for data mining.

MSC:

68P15 Database theory
68T05 Learning and adaptive systems in artificial intelligence
68P05 Data structures

Software:

LISp-Miner; GUHA
PDF BibTeX XML Cite
Full Text: DOI

References:

[1] Agrawal, R.; Manilla, H.; Sukent, R.; Toivonen, A.; Verkamo, A., Fast discovery of association rules, (), 307-328
[2] Adamo, J.M., Data mining for associational rules and sequential patterns: sequential and parallel algorithms, (2001), Springer-Verlag
[3] Aubrect, P., Mining in hepatitis data by lisp-miner and sumatratt. Porto 03.10.2005 07.10.2005, (), 8
[4] Buckley, J.J., Fuzzy statistics, (2004), Springer-Verlag Berlin · Zbl 1077.62051
[5] Buckley, J.J., Fuzzy statistics: hypothesis testing, Soft comput., 9, 512-518, (2005) · Zbl 1079.62026
[6] M. Chrz, Transparent deduction rules for the GUHA procedures, Master thesis, Faculty of Mathematics and Physics, Charles University, Prague, 2007
[7] Coufal, D., GUHA analysis of air pollution data, (), 465-468 · Zbl 1027.68740
[8] D. Coufal, M. Holeňa, A. Sochorová, Coping with discovery challenge by GUHA, in: Workshop Notes on Discovery Challenge, PKDD 1999, Prague, pp. 7-12
[9] Date, C.J., An introduction to database systems, (1976), Addison-Wesley Publishing Company · Zbl 0334.68025
[10] Coufal, D.; Matucha, P.; Uhlířová, H.; Lomský, B.; Forczek, M.T., Analysis of coniferous forest damage, effects of trichloracetic acid, sulphur, fluorine, and chlorine on needle loss of Norway spruce, Neural network world, 13, 89-102, (2003)
[11] D. Coufal, E. Turunen, Short term prediction of highway travel time using data mining and neuro-fuzzy methods, in: Proc. IFSA 2003, Istanbul, Fuzzy Sets and Systems, 2003, pp. 175-182
[12] Džeroski, S.; Lavrač, N., Relational data mining, (2001), Springer-Verlag · Zbl 1003.68039
[13] T. Feglar, The GUHA architecture, in: Proc. Relmics 6, Tilburg, The Netherlands, pp. 358-364
[14] Gottwald, S., A treatise on many-valued logics, (2001), Taylor & Francis London · Zbl 1048.03002
[15] Grzegorzewski, P.; Hryniewicz, O., Testing statistical hypotheses in fuzzy environment, Mathware and soft comput., 4, 203-217, (1997) · Zbl 0893.68139
[16] Hájek, P.; Havel, I.; Chytil, M., The GUHA method of automatic hypotheses determination, Computing, 1, 293-308, (1966) · Zbl 0168.26105
[17] Hájek, P.; Bendová, K.; Renc, Z., The GUHA method and three-valued logic, Kybernetika, 7, 421-431, (1971) · Zbl 0232.68034
[18] Hájek, P., Automatic listing of important observational statements I-II, Kybernetika, Kybernetika, 10, 95-124, (1974), 251-271, III · Zbl 0289.68047
[19] Hájek, P.; Havránek, T., On generation of inductive hypotheses, Int. J. man-Mach. stud., 9, 415-438, (1977) · Zbl 0372.68026
[20] Hájek, P.; Havránek, T., Mechanizing hypothesis formation (mathematical foundations for a general theory), (1978), Springer-Verlag, 396 p · Zbl 0371.02002
[21] Hájek, P.; Havránek, T., Mechanizing hypothesis formation (mathematical foundations for a general theory), Internet edition · Zbl 0371.02002
[22] Special issue on GUHA, Int. J. man-Mach. stud., 10, 1, (1978), P. Hájek (Guest editor):
[23] Second special issue on GUHA, Int. J. man-Mach. stud., 15, 3, (1981), P. Hájek (Guest editor):
[24] Hájek, P.; Havránek, T., GUHA 80: an application of artificial intelligence to data analysis, Comput. artificial intelligence, 1, 107-134, (1982)
[25] Hájek, P.; Ivánek, J., Artificial intelligence and data analysis, (), 54-60
[26] P. Hájek, The new version of the GUHA procedure ASSOC, in: COMPSTAT 1984, pp. 360-365
[27] Hájek, P.; Hájková, M., The expert system shell EQUANT-PC: philosophy, structure and implementation, Comput. statist. quart., 5, 4, 261-267, (1990) · Zbl 0715.68079
[28] Hájek, P.; Sochorová, A.; Zvárová, J., GUHA for personal computers, Comput. statist. data anal., 19, 149-153, (1995) · Zbl 0875.62013
[29] Hájek, P.; Holeňa, M., Formal logics of discovery and hypothesis formation by machine, (), 291-302
[30] Hájek, P., Metamathematics of fuzzy logic. studies in logic, (1998), Kluwer · Zbl 0937.03030
[31] Hájek, P., Logics for data mining (GUHA rediviva), (), Neural network world, 10, 3, 301-311, (2000), reprinted:
[32] Hájek, P., The GUHA method and mining association rules, ()
[33] Hájek, P., Relations in GUHA style data mining, (), 91-96
[34] Hájek, P.; Tulipani, S., Complexity of fuzzy probability logic, Fund. inform., 45, 207-213, (2001) · Zbl 0972.03025
[35] P. Hájek, J. Rauch, T. Feglar, D. Coufal, The GUHA method, data preprocessing and mining, in: Proc. DTDM02 (Database Technologies for Data Mining), Prague, 2002, pp. 29-36 · Zbl 1099.68690
[36] Hájek, P.; Holeňa, M., Formal logics of discovery and hypothesis formation by machine, Theoret. comput. sci., 292, 345-357, (2003) · Zbl 1018.03025
[37] Hájek, P., Relations in GUHA style data mining II, (), 242-247
[38] Hájek, P., On generalized quantifiers, finite sets and data mining, (), 489-496 · Zbl 1091.68532
[39] J. Hálová, P. Žák, Coping discovery challenge of mutagenes discovery with GUHA+/- for windows, in: The Fourth Pacific-Asia Conference on Knowledge Discovery and Data Mining, Workshop KDD Challenge 2000, Kyoto, 2000, pp. 55-60
[40] Havránek, T., The statistical modification and interpretation of GUHA method, Kybernetika, 7, 13-21, (1971) · Zbl 0216.24103
[41] Havránek, T., Statistical quantifiers in observational calculi, Theory and decision, 6, 221-230, (1975) · Zbl 0313.68070
[42] Havránek, T., Towards a model theory of statistical theories, Synthese, 36, 441-458, (1977) · Zbl 0393.62001
[43] M. Holeňa, Exploratory data processing using a fuzzy generalization of the GUHA approach, in: Applied Decision Technologies. Stream 3: Fuzzy Logic, 1995
[44] Holeňa, M., Fuzzy hypotheses testing and GUHA implicational quantifiers, Bull. stud. exchanges fuzz. appl., 63, 10-15, (1995)
[45] Holeňa, M., Exploratory data processing using a fuzzy generalization of the GUHA approach, (), 213-229
[46] M. Holeňa, A method for approximate reasoning in exploratory data analysis, in: R. Trapl, (Ed.), Proceedings of the 13th European Meeting on Cybernetics and Systems Research, vol. 1, 1996, pp. 329-334
[47] Holeňa, M., Fuzzy hypotheses for GUHA implications, Fuzzy sets and systems, 98, 101-125, (1998)
[48] Holeňa, M., A fuzzy logic framework for testing vague hypotheses with empirical data, (), 401-407
[49] Holeňa, M., A fuzzy logic generalization of a data mining approach, Neural network world, 11, 595-610, (2001)
[50] Holeňa, M., Fuzzy hypotheses testing in the framework of fuzzy logic, Fuzzy sets and systems, 145, 229-252, (2004) · Zbl 1050.68137
[51] Karban, T., Relational data mining and GUHA, (), 103-112
[52] Karban, T.; Rauch, J.; Šimůnek, M., SDS rules and association rules, ()
[53] M. Kováč, T. Kuchař, A. Kuzmin, M. Ralbovský, Ferda, New visual environment for data mining, in: Znalosti 2006, Conference on Data Mining, Hradec Králové 2006, pp. 118-129 (in Czech)
[54] J. Kodym, Classes of SD4ft-patterns, Master thesis, Faculty of Mathematics and Physics, Charles University, Prague, 2007 (in Czech)
[55] T. Kuchař, Experimental GUHA procedures, Master thesis, Faculty of Mathematics and Physics, Charles University, Prague, 2006 (in Czech)
[56] A. Kuzmin, Relational GUHA procedures, Master thesis, Faculty of Mathematics and Physics, Charles University, Prague, 2007 (in Czech)
[57] Lín, V.; Rauch, J.; Svátek, V., Contend-based retrieval of analytic reports, (), 219-224
[58] Lín, V.; Rauch, J.; Svátek, V., Analytic reports from KDD: integration into semantic web, (), 38
[59] Lín, V.; Rauch, J.; Svátek, V., Mining and querying in association rule discovery, (), 97-98
[60] Lín, V.; Dolejší, P.; Rauch, J.; Šimůnek, V., KL-miner procedure for datamining, Neural network world, 5/04, 411-420, (2004)
[61] Matheus, J., Selecting and reporting what is interesting: the KEFIR application to healthcare data, (), 495-515
[62] Nguyen, H.T., Fundamentals of statistics with fuzzy data, (2006), Springer-Verlag Berlin · Zbl 1100.62002
[63] Novák, V.; Perfilieva, I.; Močkoř, J., Mathematical principles of fuzzy logic, (1999), Kluwer Academic Publishers Dordrecht · Zbl 0940.03028
[64] L. Pecen, E. Pelikán, H. Beran, D. Pivka, Short-term fx market analysis and prediction, in: Neural Networks in Financial Engeneering, 1996, pp. 189-196 · Zbl 0936.91029
[65] Pokorný, J.; Rauch, J., The GUHA-DBS database system, Int. J. man-Mach. stud., 15, 289-298, (1981)
[66] Pudlák, P., Polynomial complete problems in the logic of automated discovery, () · Zbl 0318.68055
[67] Pudlák, Pavel; Springsteel, Frederick N., Complexity in mechanized hypothesis formation, Theoret. comput. sci., 8, 203-225, (1979) · Zbl 0404.68097
[68] Ralbovský, M.; Kuchař, T., Using disjunctions in association mining, ()
[69] Rauch, J., Ein beitrag zu der GUHA methode in der dreiwertigen logik, Kybernetika, 11, 101-113, (1975) · Zbl 0309.02013
[70] Rauch, J., Some remarks on computer realisations of GUHA, Int. J. man-Mach. stud., 10, 23-28, (1978)
[71] J. Rauch, Querry languages and mechanizing hypothesis formation, in: Proceedings of SOFSEM’79, VVS Bratislava, 1979, pp. 388-389 (in Czech)
[72] J. Rauch, Information systems and mechanizing hypothesis formation, in: Proceedings of SOFSEM’80, VVS Bratislava, 1980, pp. 397-398 (in Czech)
[73] Rauch, J., Main problems and further possibilities of the computer realizations of GUHA procedures, Int. J. man-Mach. stud., 15, 283-287, (1981)
[74] J. Rauch, Observational calculi for analysis of data in databases, in: Proceedings of SOFSEM’81, VVS Bratislava, 1981, pp. 394-396 (in Czech)
[75] J. Rauch, Logical foundations of hypothesis formation from databases, in: Mathematical Institute of the Czechoslovak Academy of Sciences, Prague, Czech Republic, Dissertation, 1986 (in Czech)
[76] Rauch, J., Logical problems of statistical data analysis in data bases, (), 53-63
[77] Rauch, J., Logical calculi for knowledge discovery in databases, ()
[78] J. Rauch, Classes of four-fold table quantifiers, in: Proc. Principles of Data Mining and Knowledge Discovery, Nantes, France, 1998, pp. 203-211
[79] J. Rauch, Four-fold table calculi and missing information, in: Proc. Joint Conference on Information Sciences, Durham, North Carolina, 1998, pp. 375-378
[80] J. Rauch, Contribution to logical foundations of KDD, Assoc. Prof. thesis, Faculty of Informatics and Statistics, University of Economics Prague, Czech Republic, 1998 (in Czech)
[81] Rauch, J., Four-fold table calculi for discovery science, ()
[82] Rauch, J., Deduction in logic of association rules, ()
[83] Rauch, J.; Šimůnek, M., Mining for 4ft rules, ()
[84] Rauch, J., Mining for statistical association rules, ()
[85] Rauch, J.; Šimůnek, M., Mining for association rules by 4ft-miner, ()
[86] J. Rauch, Interesting association rules and multi-relational association rules, in: Communications of Institute of Information and Computing Machinery, Taiwan, vol. 5, Taiwan 2002
[87] J. Rauch, M. Šimůnek, Alternative approach to mining association rules, in: Proceedings of ICDM02 Workshop The Foundation of Data Mining and Knowledge Discovery, Maebashi, Japan, 2002
[88] J. Rauch, Definability of association rules in predicate calculus, in: Proceedings of ICDM2003 WORKSHOP Foundations and New Directions of Data Mining, Melbourne, USA, 2003
[89] Rauch, J., Definability of association rules and tables of critical frequencies, () · Zbl 1158.68006
[90] Rauch, J., Logic of association rules, Applied intelligence, 22, 9-28, (2005) · Zbl 1101.68525
[91] Rauch, J., Definability association rules in predicate calculus, (), 23-40
[92] Rauch, J., Classes of association rules, an overview, () · Zbl 1161.68736
[93] Rauch, J., Many sorted observational calculi for multi-relational data mining, (), 417-422
[94] Rauch, J., Mining in health data by GUHA method. Berlin 22.09.2006, (), 71-74, [online] URL:
[95] Rauch, J., Observational calculi, classes of association rules and F-property, (), 287-293
[96] J. Rauch, Project SEWEBAR - Considerations on semantic web and data mining, accepted for presentation at the 3rd Indian International Conference on Artificial Intelligence (IICAI-07)
[97] Rauch, J.; Šimůnek, M., An alternative approach to mining association rules, (), 219-238
[98] J. Rauch, M. Šimůnek, GUHA method and granular computing, in: X. Hu, et al. (Eds.), Proceedings of IEEE Conference Granular Computing, 2005, 2005
[99] Rauch, Jan; Šimůnek, Milan; Lín, Václav, Mining for patterns based on contingency tables by KL-miner first experience, (), 155-167
[100] J. Rauch, M. Šimůnek, Dealing with background knowledge in the SEWEBAR project, in: Proceedings of the ECMLPKDD 2007 Workshop, Prior Conceptual Knowledge in Machine Learning and Data Mining, Warsaw, 2007, pp. 97-108
[101] J. Rauch, M. Šimůnek, Semantic web presentation of analytical reports from data mining - Preliminary considerations, in: WEB INTELLIGENCE, IEEE Computer Society, Los Alamitos, ISBN 0-7695-3026-5, 2007, pp. 3-7,
[102] Rauch, J., Data mining in medical data ADAMEK, (), 352-355
[103] Rauch, J.; Tomečková, M., System of analytical questions and reports on mining in health data - A case study, (), 176-181
[104] Saade, J.J.; Schwarzlander, H., Fuzzy hypotheses testing with hybrid data, Fuzzy sets and systems, 35, 213-217, (1990) · Zbl 0713.62010
[105] Strossa, P.; Černý, Z.; Rauch, J., Reporting data mining results in a natural language, (), 347-362
[106] Svátek, V.; Rauch, J.; Ralbovský, M., Ontology-enhanced association mining, (), 163-179
[107] V. Šebesta, L. Straka, Determination of suitable markers by the GUHA method for the prediction of bleeding at patients with chronic lymphoblastic leukemia, in: Medicon 98, Mediterranean Conference on Medical and Biological Engineering and Computing 8, Lemesos, Cyprus
[108] Šimůnek, M., Academic KDD project lisp-miner, ()
[109] Taheri, S.M.; Behboodian, J., A Bayesian approach to fuzzy hypotheses testing, Fuzzy sets and systems, 123, 39-48, (2001) · Zbl 0983.62015
[110] Watanabe, N.; Imaizumi, T., A fuzzy statistical test of fuzzy hypotheses, Fuzzy sets and systems, 53, 167-178, (1993) · Zbl 0795.62025
[111] J. Zvárová, J. Preiss, A. Sochorová, Analysis of data about epileptic patients using GUHA method, in: J. Zvárová, I. Malá (Eds.), EuroMISE 95, Information, Health and Education, Prague, EuroMISE Center 1995, TEMPUS International Conference, Prague, Czech Republic, 95.10.20-95.10.23, p. 87
[112] GUHA+- project web site
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.