Uživatelské nástroje

Nástroje pro tento web


Postranní lišta

Úvod

Důležité pojmy

Vztahy, s nimiž procedury pracují

GUHA procedury

GUHA procedury - společné prvky

Observační kalkuly - relevantní výsledky

Důležité tématické okruhy

lm_guha_te_publikace

Vybrané publikace

Přehled

Jsou uvedeny publikace mající vztah k metodě GUHA obecně, k systému LISp-Miner a k výzkumu spojenému s tímto systémem. Publikace jsou rozděleny do tří částí. První dvě části jsou:

V těchto částech jsou uvedeny všechny publikace, pro které se podařilo nalézt zmínku na Internetu. Není tedy uvedena řada publikací které měly vliv na vývoj metody GUHA. Mimo jiné se jedná o referáty na seminářích SOFSEM v letech 1978 až 1989 a o referáty z různých pracovních seminářů. Pokud nějaká publikace není uvedena, neznamená to, že na Internetu o ní zmínka skutečně není. Znamená to pouze, že se ji autorovi nepodařilo nelézt.

Třetí část se týká období po roce 1989, kdy je GUHA rozvíjena zejména jako metoda pro dobývání znalostí z databází.

1966 - 1981

Od počátků metody GUHA do druhého samostatného čísla o metodě GUHA v časopisu International Journal of Man-machine Studies.

P. Hájek I. Havel M. Chytil: GUHA - metoda automatického vyhledávání hypotéz I. Kybernetika 2 (1966) 1, 31-47.

P. Hájek, I. Havel, M. Chytil: The GUHA method of automatic hypotheses determination, Computing 1 (1966), pp. 293–308.

P. Hájek: Problém obecného pojetí metody GUHA. Kybernetika 4 (1968), 6, 505-515.

T. Havránek: The statistical interpretation and modification of GUHA method.(English). Kybernetika, vol. 7 (1971), issue 1, pp. 13-21

P. Hájek K. Bendová Z. Renc: The GUHA method and the three-valued logic. Kybernetika 7 (1971), 6, 421-435.

P. Hájek: Automatic listing of important observational statements. I. (English). Kybernetika, vol. 9 (1973), issue 3, pp. 187-205

P. Hájek: Automatic listing of important observational statements. II. (English). Kybernetika, vol. 9 (1973), issue 4, pp. 251-271

P. Hájek: Automatic listing of important observational statements. III.(English). Kybernetika, vol. 10 (1974), issue 2, pp. (95)-124

T. Havránek: Statistical quantifiers in observational calculi, Theory and Decision 6 (1975) 221–230.

P. Pudlák: Polynomial complete problems in the logic of automated discovery, in: Proc. Math. Foundations of Computer Science, in: Lecture Notes in Comput. Sci., vol. 32, Springer-Verlag, Heidelberg, 1975.

J. Rauch: Ein Beitrag zu der GUHA Methode in der dreiwertigen Logik. (German) [A remark to the GUHA method in the three-valued logic]. Kybernetika, vol. 11 (1975), issue 2, pp. (101)-113

P. Hájek, T. Havránek: On generation of inductive hypotheses, Int. J. Man-Mach. Stud. 9 (1977) 415–438.

T. Havránek: Towards a model theory of statistical theories, Synthese 36 (1977) 441–458.

P. Hájek, T. Havránek: Mechanizing Hypothesis Formation (Mathematical Foundations for a General Theory), Springer-Verlag, 1978, 396 p.

P. Hájek, T. Havránek: Mechanizing hypothesis formation (Mathematical foundations for a general theory), Internet edition

P. Hájek (Guest editor): Special Issue on GUHA, Int. J. Man-Mach. Stud. 10 (1) (1978).

P. Pudlák, F. N. Springsteel: Complexity in mechanized hypothesis formation, Theoret. Comput. Sci. 8 (1979) 203–225.

P. Hájek (Guest editor): Second Special Issue on GUHA, Int. J. Man-Mach. Stud. 15 (3) (1981).

1982 - 1989

P. Hájek, J. Ivánek: Artificial Intelligence and Data Analysis. In: Caussinus H., Ettinger P., Tomassone R. (eds) COMPSTAT 1982 5th Symposium held at Toulouse 1982. Physica, Heidelberg

P. Hájek, T. Havránek, M. Chytil: Metoda GUHA. Academia 1983, Praha

P. Hájek: The New Version of the GUHA Procedure ASSOC (Generating Hypotheses on Associations) — Mathematical Foundations. In: Havránek T., Šidák Z., Novák M. (eds) Compstat 1984. Physica, Heidelberg

Metoda GUHA a dobývání znalostí z databází

Po roce 1989 je metoda GUHA rozvíjena zejména jako metoda pro dobývání znalostí z databází. Publikace z tohoto období jsou rozděleny do tří částí:

Z první části jsou uvedeny všechny publikace, pro které se autorovi podařilo nalézt zmínku na Internetu. Pro další oblasti jsou vybrány pouze důležité publikace, řadu dalších lze nalézt na citačních rejstřících, například na Google Scholar.

Obecně o metodě GUHA, s ní spojené teorii a aplikacích

P. Hájek, A. Sochorová, J. Zvárová: GUHA for personal computers, Comput. Statist. Data Anal. 19 (1995) 149–153.

J. Rauch: Logical calculi for knowledge discovery in databases, in: Proceedings Principles of Data Mining and Knowledge Discovery, Springer-Verlag, 1997.

P. Hájek, M. Holeňa: Formal logics of discovery and hypothesis formation by machine, in: Arikawa Setsuo, Motoda Hiroshi (Eds.), Discovery Science, Proceedings, Springer-Verlag, Berlin, 1998, pp. 291–302.

M. Holeňa: Fuzzy hypotheses for GUHA implications, Fuzzy Sets and Systems 98 (1998) 101–125.

J. Rauch, Classes of four-fold table quantifiers, in: Proc. Principles of Data Mining and Knowledge Discovery, Nantes, France, 1998, pp. 203–211.

J. Ivánek: On the Correspondence between Classes of Implicational and Equivalence Quantifiers. In: J. Zytkow, J. Rauch(ed.). Principles of Data Mining and Knowledge Discovery. Berlin : Springer, 1999, pp. 116–124. ISBN 3-540-66490-4.

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.

P. Hájek, T. Havránek: Mechanizing Hypothesis Formation (Mathematical Foundations for a General Theory), Internet edition, 2002

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.

J. Rauch:Interesting association rules and multi-relational association rules, in: Communications of Institute of Information and Computing Machinery, Taiwan, vol. 5, Taiwan 2002.

P. Hájek: Relations in GUHA style data mining II, in: R. Berghammer, B. Möller (Eds.), Relational Methods in Computer Science, Christian-Albrechts- Universität, Kiel, 2003, pp. 242–247.

P. Hájek: On generalized quantifiers, finite sets and data mining, in: M.A. Klopotek, S.T. Wierzchon, K. Trojanowski (Eds.), Intelligent Information Processing and Web Mining, Physica-Verlag, Berlin, 2003, pp. 489–496.

P. Hájek, M. Holeňa: Formal logics of discovery and hypothesis formation by machine, Theoret. Comput. Sci. 292 (2003) 345–357.

M. Holeňa: Fuzzy hypotheses testing in the framework of fuzzy logic, Fuzzy Sets and Systems 145 (2004) 229–252.

J. Ivánek: Using Fuzzy Logic Operators for Construction of Data Mining quantifiers. Neural Network World. 2004, roč. 14, č. 5, s. 403–410. ISSN 1210-0552.

J. Rauch: Definability of association rules and tables of critical frequencies, in: T.Y. Lin, et al. (Eds.), Foundations of Data Mining, IEEE Computer Society, Brighton, 2004.

J. Ivánek: Construction of implicational quantifiers from fuzzy implications. Fuzzy Sets and System. 2005, roč. 151, č. 2, pp. 381–391. ISSN 0165-0114.

J. Rauch: Logic of association rules, Applied Intelligence 22 (2005) 9–28.

J. Rauch: Definability association rules in predicate calculus, in: T.Y. Lin, S. Ohsuga, C.J. Liau, X. Hu (Eds.), Foundations and Novel Approaches in Data Mining, Springer-Verlag, Berlin/Heidelberg/New York, 2005, pp. 23–40.

J. Rauch: Classes of association rules, an overview, in: T.Y. Lin, Y. Xie (Eds.), Foundation of Semantic Oriented Data and Web Mining, IEEE Computer Society, Houston, 2005.

J. Ivánek: Combining Implicational Quantifiers for Equivalence Ones by Fuzzy Connectives. International Journal of Intelligent Systems. 2006, roč. 21, č. 3, pp. 325–334. ISSN 0884-8173.

J. Rauch: Many sorted observational calculi for multi-relational data mining, in: Data Mining – Workshops, IEEE Computer Society, Piscataway, ISBN 0- 7695-2702-7, 2006, pp. 417–422.

M. Ralbovský, T. Kuchař, Using disjunctions in association mining, in: P. Perner (Ed.), Advances in Data Mining – Theoretical Aspects and Applications, in: Lecture Notes in Artificial Intelligence, vol. 4597, Springer-Verlag, Heidelberg, 2007.

Esko Turunen: Interpreting GUHA Data Mining Logic in Paraconsistent Fuzzy Logic Framework. Algorithmic Decision Theory 2009: 284-293

P. Hájek, M. Holeňa, J. Rauch: The GUHA method and its meaning for data mining. Journal of Computer and System Science. 2010, roč. 76, č. 1, s. 34–48. ISSN 0022-0000.

R. Piché, E. Turunen:Bayesian Assaying of GUHA Nuggets. IPMU (1) 2010: 348-355

J. Ivánek: Some properties of evaluated implications used in knowledge-based systems and data-mining. Journal of Systems Integration [online]. 2012, roč. 3, č. 3, pp. 1–7. ISSN 1804-2724.

J. Rauch: Observational Calculi and Association Rules. 1. vyd. Berlin : Springer-Verlag, 2013. 296 s. ISBN 978-3-642-11736-7. ISSN 1860-949X.

J. Ivánek: Triads of implicational double-implicational and equivalency data-mining quantifiers. In: Proceedings of the 16th Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty. Jindřichův Hradec : Faculty of Mangement University of Economics, 2013, pp. 145–151. ISBN 978-80-245-1950-0.

J. Ivánek:Selection and correction of weighted rules based on Lukasiewicz's fuzzy logic with evaluated syntax. Kybernetika. 2017, roč. 53, č. 1, s. 113–118. ISSN 0023-5954. DOI: 10.14736/kyb-2017-1-0113.

LISp-Miner a jeho aplikace obecně

J. Rauch, M. Šimůnek: Mining for 4ft rules, in: Proceedings of Discovery Science, Springer-Verlag, 2000.

M. Šimůnek: Academic KDD project LISp-Miner, in: A. Abraham, et al. (Eds.), Advances in Soft Computing – Intelligent Systems Design and Applications, Springer-Verlag, Berlin/Heidelberg/New York, 2003.

P. Strossa, J. Rauch: Converting Association Rules into Natural Language — an Attempt. in: Kłopotek M.A., Wierzchoń S.T., Trojanowski K. (eds) Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol 22. Springer, Berlin, Heidelberg, 2003, pp. 383–392

P. Strossa, Z. Černý, J. Rauch: Reporting data mining results in a natural language, in: T.Y. Lin, S. Ohsuga, C.J. Liau, X. Hu (Eds.), Foundations of Data Mining and Knowledge Discovery, Springer-Verlag, Berlin, 2005, pp. 347–362.

J. Rauch, M. Šimůnek, V. Lín: Mining for patterns based on contingency tables by KL-Miner - first experience, in: Tsau Young Lin, Setsuo Ohsuga, C.J. Liau, Xiaohua Hu (Eds.), Foundations and Novel Approaches in Data Mining, Springer-Verlag, Berlin, ISBN 3-540-28315-3, 2005, pp. 155–167.

J. Rauch: Logical Aspects of the Measures of Interestingness of Association Rules. In: J. Koronacki et all (eds.) Advances in Machine Learning II. Berlin : Springer Verlag, 2010, pp. 175–203. ISBN 978-3-642-05178-4.

J. Rauch, M. Šimůnek: An alternative approach to mining association rules, in: T.Y. Lin, et al. (Eds.), Data Mining, Foundations, Methods, and Applications, Springer-Verlag, 2005, pp. 219–238.

T. Kliegr, J. Rauch: An XML Format for Association Rule Models Based on the GUHA Method. In: Semantic Web Rules. Berlin : Springer Verlag, 2010, pp. 273–288. ISBN 978-3-642-16288-6. ISSN 0302-9743.

P. Berka: ETree Miner: A New GUHA Procedure for Building Exploration Trees. In: Foundations of Intelligent Systems.New York : Springer, 2011, pp. 96–101. ISBN 978-3-642-21915-3. ISSN 0302-9743. DOI: 10.1007/978-3-642-21916-0_11.

J. Rauch: Metoda GUHA a dobývání znalostí z databází. In: V. MAŘÍK a kol.: Umělá inteligence 6. Praha : Academia, 2013, s. 348–391. 490 s. ISBN 978-80-200-2276-9.

M. Šimůnek: Příprava umělých dat pro výuku a testování pomocí evolučního algoritmu. Systémová integrace. 2013, roč. 20, č. 2, s. 67–80. ISSN 1210-9479.

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

J. Rauch, M. Šimůnek: LAREDAM – Considerations on System of Local Analytical Reports from Data Mining. In: Foundations of Intelligent Systems. Berlin : Springer-Verlag, 2008, pp. 143–149. ISBN 978-3-540-68122-9. ISSN 0302-9743. DOI: 10.1007/978-3-540-68123-6_16.

J. Rauch, M. Šimůnek: Action Rules and the GUHA Method: Preliminary Considerations and Results. In: Foundations of Intelligent Systems. Berlin : Springer Verlag, 2009, pp. 76–87. ISBN 978-3-642-04124-2. ISSN 1867-8211. DOI: 10.1007/978-3-642-04125-9_11.

M. Šimůnek, T. Tammisto: Distributed Data-Mining in the LISp-Miner System Using Techila Grid. In: Networked Digital Technologies.Berlin : Springer-Verlag, 2010, s. 15–20. ISBN 978-3-642-14291-8. ISSN 1865-0929. DOI: 10.1007/978-3-642-14292-5_3.

P. Berka, J. Rauch: Meta-learning for Post-processing of Association Rules. In: Data Warehousing and Knowledge Discovery. Berlin : Springer Verlag, 2010, pp. 251–262. ISBN 978-3-642-15104-0. DOI: 10.1007/978-3-642-15105-7_20.

M. Šimůnek: Nová GUHA-procedura ETree-Miner v systému LISp-Miner. Systémová integrace. 2012, roč. 19, č. 2, s. 62–72. ISSN 1210-9479.

M. Šimůnek: LISp-Miner Control Language description of scripting language implementation. Journal of systems integration. 2014, roč. 5, č. 2, s. 28–44. ISSN 1804-2724.

J. Rauch, M. Šimůnek: Dobývání znalostí z databází, LISp-Miner a GUHA. 1. vyd. Praha : Oeconomica, 2014. 462 s. ISBN 978-80-245-2033-9.

R. Piché, M. Järvenpää, E. Turunen, M. Šimůnek:Bayesian analysis of GUHA hypotheses. J. Intell. Inf. Syst. 42(1): 47-73 (2014)

J. Rauch, M. Šimůnek: Data Mining with Histograms – A Case Study. In: Foundations of Intelligent Systems [online]. Lyon, 21.10.2015 – 23.10.2015. Cham : Springer International Publishing, 2015, s. 3–8. ISBN 978-3-319-25251-3. DOI: 10.1007/978-3-319-25252-0.

P. Berka: Using the LISp-Miner System for Credit Risk Assessment. Neural Network World. 2016, roč. 26, č. 5, s. 497–518. ISSN 1210-0552. DOI: 10.14311/NNW.2016.26.029.

P. Berka: Practical Aspects of Data Mining Using LISp-Miner. Computing and Informatics. 2016, roč. 35, č. 3, s. 528–554. ISSN 1335-9150.

J. Rauch, M. Šimůnek: Apriori and GUHA – Comparing two approaches to data mining with association rules. Intelligent Data Analysis [online]. 2017, roč. 21, č. 4, s. 981–1013. ISSN 1088-467X. DOI: 10.3233/IDA-160069.

E. Turunen:Using GUHA Data Mining Method in Analyzing Road Traffic Accidents Occurred in the Years 2004-2008 in Finland. Data Science and Engineering 2(3): 224-231 (2017)

E. Turunen: Paraconsistent Many-Valued Logic in GUHA Framework. Acta Informatica Pragensia 7(1) (2018)

Automatizace procesu DZD a využití doménových znalostí

J. Rauch: Considerations on Logical Calculi for Dealing with Knowledge in Data Mining. In: Z. W. Ras, A. Dardzinska (eds.) Advances in Data Management. Berlin : Springer-Verlag, 2009, pp. 177–201. Studies in Computational Intelligence 223/2009. ISBN 978-3-642-02189-3. ISSN 1860-949X.

J. Rauch, M. Šimůnek: Dealing with Background Knowledge in the SEWEBAR Project. In: B. Berendt et all. (ed.). Knowledge Discovery Enhanced with Semantic and Social Information [online]. Berlin : Springer-Verlag, 2009, pp. 89–106. ISBN 978-3-642-01890-9. ISSN 1860-949X.

J. Rauch, M. Šimůnek: Applying Domain Knowledge in Association Rules Mining Process – First Experience. In: ISMIS 2011 Foundations of Intelligent Systems. London : Springer, 2011, s. 113–122. ISBN 978-3-642-21915-3. ISSN 0302-9743. DOI: 10.1007/978-3-642-21916-0_13.

J. Rauch: EverMiner: consideration on knowledge drive permanent data mining process. International Journal of Data Mining, Modelling and Management [online]. 2012, roč. 4, č. 3, s. 224–243. ISSN 1759-1163. eISSN 1759-1171.

J. Rauch: Domain Knowledge and Data Mining with Association Rules – A Logical Point of View. In: Foundations of Intelligent Systems. Berlin : Springer Verlag, 2012, pp. 11–20. ISBN 978-3-642-34623-1. ISSN 0302-9743. DOI: 10.1007/978-3-642-34624-8_2. eISBN 978-3642-34624-8 eISSN 1611-3349.

J. Rauch, M. Šimůnek: Learning Association Rules from Data through Domain Knowledge and Automation. In: Rules on the Web. From Theory to Applications Springer International Publishing Switzerland, 2014, s. 266–280. ISBN 978-3-319-09869-2. ISSN 0302-9743. DOI: 10.1007/978-3-319-09870-8_20.

M. Šimůnek, J. Rauch: EverMiner Prototype Using LISp-Miner Control Language. In: Foundations of Intelligent Systems, Springer International Publishing Switzerland, 2014, s. 113–122. ISBN 978-3-319-08325-4. ISSN 0302-9743. DOI: 10.1007/978-3-319-08326-1_12.

J. Rauch: Formal Framework for Data Mining with Association Rules and Domain Knowledge – Overview of an Approach. Fundamenta Informaticae [online]. 2015, roč. 137, č. 2, s. 171–217. ISSN 0169-2968. DOI: 10.3233/FI-2015-1175.

J. Rauch: Logical Aspects of Dealing with Domain Knowledge in Data Mining with Association Rules. Fundamenta Informaticae. 2016, roč. 148, č. 1–2, s. 1–33. ISSN 0169-2968. DOI: 10.3233/FI-2016-1420.

V. Nekvapil: Data Mining with Trusted Knowledge. In: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems.Praha : ACSIS, 2017, s. 9–16. ISBN 978-83-922646-2-0. ISSN 2300-5963. DOI: 10.15439/2017F216.

V. Nekvapil: Applying Trusted Knowledge in Evaluation Phase of Data Mining. In: Data a znalosti 2017. Plzeň : ZU Plzeň, 2017, s. 198–203. ISBN 978-80-261-0720-0.

J. Rauch: Expert deduction rules in data mining with association rules: a case study. Knowledge and Information Systems. 2018, roč. 55, č. 177, s. 1–29. eISSN 0219-3116. ISSN 0219-1377. DOI: 10.1007/s10115-018-1206-x.

V. Nekvapil, O. Zamazal: Ontologies in Support of Data Mining. In: Proceedings of the Posters and Demos Track of the 14th International Conference on Semantic Systems co-located with the 14th International Conference on Semantic Systems (SEMANTiCS 2018). Vienna, Austria, September 10-13, 2018.

J. Rauch, M. Šimůnek: Data Mining with Histograms and Domain Knowledge – Case Studies and Considerations. Fundamenta Informaticae [online]. 2019, roč. 166, č. 4, s. 349–378. eISSN 1875-8681. ISSN 0169-2968. DOI: 10.3233/FI-2019-1805.

L. Powell, A. Gelich, Z. W. Ras: The Construction of Action Rules to Raise Artwork Prices. In: Foundations of Intelligent Systems, ISMIS 2020, LNCS, volume 12117, pp. 11-20

lm_guha_te_publikace.txt · Poslední úprava: 2021/03/25 22:51 autor: jra