Formal logics of discovery and hypothesis formation by machine

Theoretical Computer Science

Volumn 292, Issue 2, 2003, Pages 345-357

  Hájek, Petr ^a   Holeňa, Martin ^a  

^a INSTITUTE OF COMPUTER SCIENCE   (Czech Republic)

Author keywords

Data analysis; Data Mining; Fuzzy logic; GUHA method; Hypothesis generation; Logic of discovery

Indexed keywords

DATA MINING; DATA REDUCTION; FUZZY SETS; SEMANTICS;

HYPOTHESIS GENERATION;

FORMAL LOGIC;

EID: 0037467664     PISSN: 03043975     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0304-3975(02)00175-5     Document Type: Conference Paper

References (64)

1. R. Agrawal, R. Srikant, Fast algorithms for mining association rules, in: Proc. 20th Internat. Conf. on Very Large Data Bases, 1994.
2. von Altrock C. Fuzzy Logic and Neurofuzzy Applications Explained. 1995;Prentice-Hall, PTR, Upper Saddle River, NJ.
3. B.G. Buchanan, Logics of Scientific Discovery, Stanford AI Memo No. 47, Stanford University 1966.
4. Chatfield C. Model uncertainty, data mining and statistical inference. J. Roy. Statist. Soc. Ser. A. 158:1995;419-466.
5. Chau T., Wong A.K.C. Pattern discovery by residual analysis and recursive partitioning. IEEE Trans. Knowledge Data Eng. 11:1999;833-852.
6. CODASYL Data Base Task Group, DBTG Report, Technical Report, ACM, 1971.
7. J. Doubravová, A. Sochorová, Testing interpersonal hypothesis of music using GUHA method. Languages Des., 1996.
8. Fayyad U., Piatetsky-Shapiro G., Smyth P. From data mining to knowledge discovery: an overview. Fayyad U., Piatetsky-Shapiro G., Smyth P., Uthurusamy R. Advances in Knowledge Discovery and Data Mining. 1996;1-36 AAAI Press, Menlo Park, CA.
9. Frawley W., Piatetsky-Shapiro G., Matheus C. Knowledge discovery in databases. an overview Piatetsky-Shapiro G., Frawley W. Knowledge Discovery in Databases. 1991;1-27 AAAI Press, Menlo Park, CA.
10. Glymour C., Madigan D., Pregibon D., Smyth P. Statistical inference and data mining. Comm. ACM. 39:1996;35-41.
11. Godo L., Esteva F., Hájek P. Reasoning about probability using fuzzy logic. Neural Network World. 10:2000;811-824.
12. P. Hájek, On logics of discovery, in: Mathematical Foundations of Computer Science, Lecture Notes in Computer Science, Vol. 32, 1975, Springer, Berlin, pp. 30-45.
13. Hájek P. Decision problems of some statistically motivated monadic modal calculi. Internat. J. Man-Mach. Stud. 15:1981;351-358.
14. P. Hájek, The new version of the GUHA procedure ASSOC (generating hypotheses on associations) - mathematical foundations, in: COMPSTAT 1984 - Proc. in Computational Statistics, 1984, pp. 360-365.
15. Hájek P. Metamathematics of Fuzzy Logic. 1998;Kluwer Academic Publishers, Dordrecht.
16. Hájek P., Esteva F., Godo L. Fuzzy logic and probability. Bernard P., Hanks S. Uncertainty and Artificial Intelligence '95. 1995;237-244 Morgan Kaufmann, San Mateo.
17. Hájek P., Havel I., Chytil M. The GUHA-method of automatic hypotheses determination. Computing. 1:1966;293-308.
18. Hájek P., Havránek T. On generation of inductive hypotheses. Internat. J. Man-Mach. Stud. 9:1977;415-438.
19. P. Hájek, T. Havránek, Mechanizing Hypothesis Formation (Mathematical Foundations for a General Theory), Springer, Berlin, Heidelberg, New York, 1978; also www.cs.cas.cz/̃hajek/guhabook.
20. Hájek P., Holeňa M. Formal logics of discovery and hypothesis formation by machine. Arikawa S., Motoda H. Discovery Science. 1998;291-302 Springer, Berlin, Tokyo.
21. Hájek P., Sochorová A., Zvárová J. GUHA for personal computers. Comput. Statist. Data Anal. 19:1995;149-153.
22. Hálová J., Štrouf O., Žák P., Sochorová A., Uchida N., Yuzuvi T., Sakakibava K., Hirota M. QSAR of catechol analogs against malignant melanoma using fingerprint descriptors. Quant. Struct.-Act. Relat. 17:1998;37-39.
23. Harmancová D., Holeňa M., Sochorová A. Overview of the GUHA method for automating knowledge discovery in statistical data sets. Noirhomme-Fraiture M. Knowledge Extraction and Symbolic Data Analysis. 1999;65-77 Eurostat, Luxembourg.
24. Havránek T. The approximation problem in computational statistics. Bečvář J. Mathematical Foundations of Computer Science '75. Lecture Notes in Computer Science. Vol. 32:1975;258-265.
25. Havránek T. Statistical quantifiers in observational calculi: an application in GUHA method. Theory and Decision. 6:1975;213-230.
26. Havránek T. Towards a model theory of statistical theories. Synthese. 36:1977;441-458.
27. Holeňa M. Exploratory data processing using a fuzzy generalization of the GUHA approach. Baldwin J. Fuzzy Logic. 1996;213-229 Wiley, New York.
28. Holeňa M. Fuzzy hypotheses for GUHA implications. Fuzzy Sets and Systems. 98:1998;101-125.
29. M. Holeňa, Traditional and modern artificial intelligence explores ecological data, in: H. Hyötyniemi, (Ed.), STeP 2000: Millenium of Artificial Intelligence, 2000.
30. Holeňa M. Observational logic integrates data mining based on statistics and neural networks. Zighed D.A., Komorowski J., Zytkov J.M. Principles of Data Mining and Knowledge Discovery. 2000;440-445 Springer, Berlin.
31. M. Holeňa, A fuzzy logic framework for testing vague hypotheses with empirical data, in: Proc. Fourth Internat. ICSC Symp. on Soft Computing and Intelligent Systems for Industry, ICSC Academic Press, Sliedrecht, 2001 pp. 401-407.
32. M. Holeňa, Statistical, logic-based, and neural networks based methods for mining rules from data, in: A.K. Hyder, V. Bystritskii (Eds.), Multisensor and Sensor Data Fusion, NATO Science Series Publishers, in preparation.
33. Holeňa M., Sochorová A., Zvárová J. Increasing the diversity of medical data mining through distributed object technology. Kokol P., Zupan B., Stare J., Premik M., Engelbrecht R. Medical Informatics Europe '99. 1999;442-447 IOS Press, Amsterdam.
34. M. Holsheimer, A. Siebes, Data mining, The search for knowledge in databases, Technical Report, CWI, Amsterdam, 1994.
35. M. Houtsma, A. Swami, Set-oriented mining of association rules, Technical Report, IBM Almaden Research Center, 1993.
36. M. Kamber, J. Han, J. Chiang, Using data cubes for metarule-guided mining of multi-dimensional association rules, Technical Report, Database Systems Research Laboratory, Simon Fraser University, 1997.
37. Keisler U.J. Probability quantifiers. Barwise J., Feferman S. Model-Theoretic Logics. 1985;539-556 Springer, New York.
38. Klösgen W. Efficient discovery of interesting statements in databases. J. Intell. Inform. Systems. 4:1995;53-69.
39. Klösgen W. Explora. a multipattern and multistrategy discovery assistant Fayyad U., Piatetsky-Shapiro G., Smyth P., Uthurusamy R. Advances in Knowledge Discovery and Data Mining. 1996;249-272 AAAI Press, Menlo Park, CA.
40. Korn F., Labrinidis A., Kotidis Y., Faloutsos C. Quantifiable data mining using ration rules. VLDB J. 8:2000;254-266.
41. Kruse R., Meyer K.D. Statistics with Vague Data. 1987;Reidel, Dordrecht.
42. D.I. Lin, Z. Kedem, Pincer search: a new algorithm for discovering the maximum frequent set, in: Proc. EDBT'98: Sixth Internat. Conf. on Extending Database Technology, 1998.
43. H. Mannila, K. Räihä, Dependency inference, in: Proc. 13th Internat. Conf. on Very Large Data Bases, 1987, pp. 155-158.
44. Mannila H., Toivonen H., Verkamo I. Efficient algorithms for discovering association rules. Fayyad U., Uthurusamy R. Knowledge Discovery in Databases. 1994;181-192 AAAI Press, Menlo Park, CA.
45. A. Mueller, Fast sequential and parallel algorithms for association rule mining: a comparison, Technical Report, Department of Computer Science, University of Maryland, College Park, 1995.
46. Pecen L., Eben K. Non-linear mathematical interpretation of the oncological data. Neural Network World. 6:1996;683-690.
47. 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.
48. Pecen L., Ramešová N., Pelikán E., Beran H. Application of the GUHA method on financial data. Neural Network World. 5:1995;565-571.
49. Plotkin G.D. A further note on inductive generalization. Mach. Intell. 6:1971;101-124.
50. Popper K.R. The Logic of Scientific Discovery. 1974;Hutchinson, London.
51. Pudlák P., Springsteel F. Complexity in mechanizing hypothesis formation. Theoret. Comput. Sci. 8:1979;203-225.
52. J. Rauch, Logical problems of statistical data analysis in data bases, in: Proc. 11th Seminar on Data Base Management Systems, 1988, pp. 53-63.
53. Rauch J. Logical calculi for knowledge discovery in databases. Zytkov Komarowski, Principles of Data Mining and Knowledge Discovery. Lecture Notes in AL. Vol. 1263:1997;Springer, Berlin.
54. Rauch J. Classes of four-fold table quantifiers. Quafafou, Zytkov Principles of Data Mining and Knowledge Discovery. 1998;203-210 Springer, Berlin.
55. R. Srikant, Q. Vu, R. Agrawal, Mining association rules with item constraints, in: Proc. Third Internat. Conf. on Knowledge Discovery and Data Mining, KDD-97, 1997.
56. Teller A., Veloso M. Program evolution for data mining. Internat. J. Expert Systems. 8:1995;216-236.
57. H. Toivonen, Discovery of frequent patterns in large data collections, Ph.D. Thesis, University of Helsinki, 1996.
58. Tukey J.W. Exploratory Data Analysis. 1977;Addison-Wesley, Reading, MA.
59. Yager R. On a semantics for neural networks based on fuzzy quantifiers. Internat. J. Intell. Systems. 7:1992;765-786.
60. Zadeh L.A. What is soft computing? (Editorial) Soft Comput. 1:1997;1.
61. Zaki M., Parathasarathy S., Ogihara M., Li W. New parallel algorithms for fast discovery of association rules. Data Mining Knowledge Discovery. 1:1997;343-373.
62. Zembowicz R., Zytkov J. From contingency tables to various forms of knowledge in databases. Fayyad U., Piatetsky-Shapiro G., Smyth P., Uthurusamy R. Advances in Knowledge Discovery and Data Mining. 1996;329-352 AAAI Press, Menlo Park, CA.
63. Zvárová J., Preiss J., Sochorová A. Analysis of data about epileptic patients using Guha method. Internat. J. Medical Inform. 45:1997;59-64.
64. Zytkov J., Zembowicz R. Contingency tables as the foundation for concepts, concept hierarchies and rules: the 49er system approach. Fund. Inform. 30:1997;383-399.