References - LISp

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References - LISp
LISp-Miner project: http://lispminer.vse.cz
References
[Ag 96]
Aggraval, R. et al: Fast Discovery of Association Rules. In (Fayyad, U. M. et al., eds.)
Advances in Knowledge Discovery and Data Mining. AAAI Press / The MIT Press, 1996,
pp. 307–328.
[BI 94]
Berka, P. – Ivánek, J.: Automated knowledge acquisition for PROSPECTOR-like expert
systems. In. (Bergadano, de Raedt eds.) Proc. ECML'94, Springer 1994, pp. 339–342.
[BR 98]
Berka, P. – Rauch, J.: Data Mining using GUHA and KEX. In proceedings of World Multiconference on Systemics, Cybernetics and Informatics. Red. Callaos, N. – Yang, T. –
Aguilar, J. Orlando Florida 1998, pp. 238–244.
[BR 03]
Burian, J. – Rauch, J.: Analysis of Death Causes in the STULONG Data Set. In: BERKA,
P. (ed.). Discovery Challenge. Zagreb: IRB, 2003, pp. 47–58. ISBN 953-6690-38-1.
[Bu 03]
Burian, J: Datamining and AA (Above Average) quantifier. In: SVÁTEK, Vojtěch (ed.).
Znalosti 2003. Ostrava: VŠB TU Ostrava, 2003, pp. 297–302. ISBN 80-248-0229-5. (In
Czech)
[Bu 04]
Burian, J: Unsupervised learning and the identification of classification attribute using
the attribute dependency. In: SNÁŠEL, Václav (ed.). Znalosti 2004 – poster proceedings.
Ostrava: VŠB TU, 2004, pp. 1–4. (In Czech)
[CDRS 03]
Černý, Z. – Dolejší, P. – Rauch, J. – Šebek, M.: Knowledge Discovery in Medical Data –
Case Study. In: SVÁTEK, Vojtěch. (ed.). Znalosti 2003. Ostrava: TU Ostrava, 2003, pp.
182–191. ISBN 80-248-0229-5. (In Czech)
[Ce 03]
Černý, Z.: WWW support for applications of system LISp-Miner, Master thesis, University
of Economics Prague, 2003, 81 Pages. (In Czech)
[COL 03]
Flach, P. – Blockeel, H. – Gartner, T. – Grobelnik, M. – Kavšek, B. – Kejkula, M. –
Krzywania, D. – Lavrač, N. – Ljubič, P. – Mladenič, D. – Moyle, S. – Raeymaekers, S. –
Rauch, J. – Rawles, S.: On the Road to Knowledge. In: MLADENIC, Dunja, LAVRAČ,
Nada, BOHANEC, Marko, MOYLE, Steve (ed.). Data mining and Decision Support.
Integration and Collaboration. Boston: Kluwer Academic Publishers, 2003, pp. 143–155.
ISBN 1-4020-7388-7.
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LISp-Miner project: http://lispminer.vse.cz
[DG 79]
Duda, R.O. – Gasching, J.E.: Model design in the Prospector consultant system for mineral
exploration. in: Michie, D. (ed.), Expert Systems in the Micro Electronic Age, Edinburgh
University Press, UK, 1979.
[DLRS 02]
Dolejší, P. – Lín, V. – RAUCH, J. – Šebek, M.: System of KDD Tasks and Results within
the STULONG Project. In: BERKA, Petr (ed.). Discovery Challenge Workshop Notes.
ECML/PKDD – 2002. Helsinki: University of Helsinki, 2002. ISBN 952-10-0639-0.
[GH+−]
GUHA+− project web site http://www.cs.cas.cz/ics/software.html
[Ha 66A]
Hajek P. – Havel I. – Chytil M.: The GUHA method of automatic hypotheses determination,
Computing 1(1966) 293–308.
[Ha 66B]
Hájek, P. – Havel, – Chytil, M.: GUHA – method of a systematic search for hypotheses,
Kybernetika 2, 1996, pp.31–47 (in Czech).
[Ha 78]
Hájek, P. – Havránek, T.: Mechanising Hypothesis Formation – Mathematical Foundations
for a General Theory. Berlin – Heidelberg – New York, Springer-Verlag, 1978, 396 pp.
The full text of the book in PDF format is available here.
[Ha 83]
Hájek, P. – Havránek, T. – Chytil, M.: GUHA Method. Praha, Academia, 1983, 314 pp (in
Czech).
[Ha 85]
Hájek, P.: Combining functions for certainty factors in consulting systems. Int.J. ManMachine Studies 22,1985, pp.59–76.
[HR 99]
Hájek, P. – Rauch J.: Logics and Statistics for Association Rules and Beyond. In: ZYTKOW,
Jan, RAUCH, Jan (ed.). Principles of Data Mining and Knowledge Discovery. Berlin:
Springer, 1999, pp. 586–587. ISBN 3-540-66490-4.
[HSZ 95]
Hájek, P. – Sochorová, A. – Zvárová, J.: GUHA for personal computers. Computational
Statistics & Data Analysis 19, (1995) pp.149–153.
[Hv 81]
Havránek, T.: The present state of the GUHA software. International Journal of ManMachine Studies, 15, (1981), pp.253–264.
[HMS 01]
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LISp-Miner project: http://lispminer.vse.cz
Hand, D. – Manilla, H. – Smyth P.: Principles of Data Mining, MIT 2001.
[IS 88]
Ivánek, J. – Stejskal, B.: Automatic acquisition of knowledge base from data without expert:
ESOD (Expert System from Observational Data). In: Proc. COMPSTAT'88 Copenhagen,
Physica-Verlag, 1988, pp.175–180.
[Iv 99]
Ivánek, J.: On the Correspondence between Classes of Implicational and Equivalence
Quantifiers. In Principles of Data Mining and Knowledge Discovery. Red. Zytkow, J. –
Rauch, J. Berlin, Springer Verlag 1999, pp. 116–124
[Ka 04]
Karban, T.: SDS-rules. In: SNÁŠEL, Václav (ed.). Znalosti 2004 – poster proceedings.
Ostrava: VŠB TU, 2004, pp. 17–20. (In Czech)
[Ke 04]
Kejkula, M.: Foundation of Interpretations of Assocional Rules. In: SNÁŠEL, Václav
(ed.). Znalosti 2004 – poster proceedings. Ostrava: VŠB TU, 2004, pp. 25–28. (In Czech)
[KRS 04]
Karban, T. – Rauch, J. – Šimůnek, M.: SDS-Rules and Association Rules.
[LRS 02A]
Lín, V. – Rauch, J. – Svátek, V.: Contend-based Retrieval of Analytic Reports. In:
SCHROEDER, Michael, WAGNER, Gerd (ed.). Rule Markup Languages for Business
Rules on the Semantic Web. Sardinia: ISWC, 2002, pp. 219–224.
[LRS 02B]
Lín, V. – Rauch, J. – Svátek, V.: Analytic Reports from KDD: Integration into Semantic
Web. In: ISWC 2002. Cagliari: University of Cagliari, 2002, p. 38.
[LRS 02C]
Lín, V. – Rauch, J. – Svátek, V.: Mining and Querying in Association Rule Discovery. In:
KLEMETTINEN, Mika, MEO, Rosa, GIANNOTTI, Fosca, DE RAEDT, Luc (ed.).
Knowledge Discovery in Inductive Databases – KDID '02. Helsinki: University of Helsinki,
2002, pp. 97–98. ISBN 952-10-0638-2.
[Ra 71]
Rauch, J.: Application of three-valued logic for GUHA method. Diploma work. Faculty of
mathematics and Physics Charles University Prague, 1971 42 pp, (in Czech).
[Ra 78]
Rauch, J.: Some Remarks on Computer Realisations of GUHA Procedures. International
Journal of Man-Machine Studies, 10, (1978), pp. 23–28.
[Ra 81]
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LISp-Miner project: http://lispminer.vse.cz
Rauch, J.: Main Problems and Further Possibilities of the Computer Realizations of GUHA
Procedures. International Journal of Man-Machine Studies, 15, 1981, pp. 283–287.
[Ra 86]
Rauch, J.: Logical Foundations of Hypothesis Formation from Databases, Mathematical
Institute of the Czechoslovak Academy of Sciences, Prague, Czech Republic, PhD. thesis,
1986 (in Czech).
[PR 81]
Pokorný, J. – Rauch, J.: The GUHA-DBS database system. International Journal of ManMachine Studies, 15, 1981, pp. 289–298.
[Ra 96]
Rauch, J.: GUHA as a Data Mining Tool. In: Practical Aspects of Knowledge Management.
Schweizer Informatiker Gesellshaft Basel, 1996.
[Ra 97]
Rauch, J.: Logical Calculi for Knowledge Discovery in Databases. In Principles of Data
Mining and Knowledge Discovery. Red. Komorowski, J. – Zytkow, J. Berlin, Springer
Verlag 1997, pp. 47–57.
[Ra 98A]
Rauch, J.: Classes of Four Fold Table Quantifiers. In Principles of Data Mining and
Knowledge Discovery. Red. Zytkow, J – Quafafou, M. Berlin, Springer Verlag 1998, pp.
203–211.
[Ra 98B]
Rauch, J.: Four-fold Table Calculi and Missing Information. In JCIS'98 Proceedings, (Paul
P. Wang, editor), Association for Intelligent Machinery, pp. 375-378, 1998.
[Ra 98C]
Rauch, J.: Contribution to Logical Foundations of KDD: Inaugural Dissertation, University
of Economics, Prague, 1998. 142 pp., (in Czech).
[Ra 98D]
Rauch, J.: Four-Fold Table Calculi for Discovery Science. In: ARIKAWA, Setsuo,
MOTODA, Hiroshi (ed.). Discovery Science. Berlin : Springer, 1998, pp. 405–406. ISBN
3-540-65390-2.
[Ra 99]
Rauch, J.: Deduction in Logic of Association Rules. Lecture Notes in Computer Science
1742. ISBN 3-540-66856-X.
[RS 00]
Rauch, J. – Simunek, M.: Mining for 4ft Association Rules. In Discovery Science 2000.
Red. Arikawa, S. – Morishita S. Springer Verlag 2000, pp. 268–272.
[Ra 01A]
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LISp-Miner project: http://lispminer.vse.cz
Rauch, J.: Mining for Statistical Association Rules. In The Fifth Pacific-Asia Conference
on Knowledge Discovery and Data Mining Industrial Track and Workshop Proceeding
Red. Joseph Fong ang Michael Ng Hong Kong 2001, pp. 149–158.
[Ra 01B]
Rauch, J.: Association Rules and Mechanizing Hypothesis Formation. Working notes of
ECML'2001 Workshop: Machine Learning as Experimental Philosophy of Science. See
also http://www.informatik.uni-freiburg.de/~ml/ecmlpkdd/.
[Ra 01C]
Rauch, J.: Mining for Association Rules in Financial Data. In: Seminar on Data Mining
for Decision Support in Marketing. Porto : LIACC, 2001.
[Ra 01D]
Rauch, J.: System LISp-Miner – Example of Application. Acta Oeconomica Pragensia,
2001, Vol. 9, No 1, pp. 125–153. ISSN 0572-3043. (In Czech)
[Ra 02A]
Rauch, J.: Mining for Scientific Hypotheses. In Meij, J.(Editor): Dealing with the data
flood. Mining Data, Text and Multimedia. STT/Beweton, The Hague. 2002. pp. 73–84.
[Ra 02B]
Rauch, J.: Interesting Association Rules and Multi-relational Association Rules. Communications of Institute of Information and Computing Machinery, Taiwan. Vol. 5, No. 2,
May 2002, pp. 77–82.
[Ra 03]
Rauch, J.: Definability of Association Rules in Predicate Calculus. In: LIN, Tsau Young,
HU, Xiaohua, OHSUGA, Setsuo, LIAU, C. J. (ed.). Data mining – Foundations and New
Directions in Data Mining. Melbourne: IEEE Computer Society, 2003, pp. 148–155.
[RS 01A]
Rauch, J. – Šimůnek, M.: Mining for 4ft Rules. In: ARIKAWA, Setsuo, MORISHITA,
Shinichi (ed.). Discovery Science. Berlin: Springer, 2000, pp. 268–272. ISBN 3-540-413529.
[RS 01B]
Rauch, J. – Simunek, M.: Mining for 4ft Association Rules by 4ft-Miner. in: INAP 2001,
The Proceeding of the International Conference On Applications of Prolog. Prolog Association of Japan, Tokyo October 2001, pp. 285–294.
[RS 02]
Rauch, J. – Šimůnek, M.: Alternative Approach to Mining Association Rules. In: LIN,
Tsau Young, OHSUGA, Setsuo (ed.). The Foundation of Data Mining and Knowledge
Discovery (FDM02). Maebashi: Izumo, 2002, pp. 157–162. ISBN 4-947717-02-6.
[RS 03]
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LISp-Miner project: http://lispminer.vse.cz
Rauch, J. – Šimůnek, M.: System LISp-Miner. In: SVÁTEK, Vojtěch (ed.). Znalosti 2003.
Ostrava: TU Ostrava, 2003, pp. 83–92. ISBN 80-248-0229-5. (In Czech)
[RS 04]
Rauch, J – Šimůnek, M.: Project LISp-miner – current state and further development. In:
SNÁŠEL, Václav (ed.). Znalosti 2004 – poster proceedings. Ostrava: VŠB TU, 2004, pp.
81–84. (In Czech)
[RSDL 04]
Rauch, J. – Šimůnek, M. – Dolejší, P. – Lín, V.: Data mining procedure KL-Miner. In:
SNÁŠEL, Václav (ed.). Znalosti 2004. Ostrava: VŠB TU, 2004, pp. 350–361. ISBN 80248-0456-5. (In Czech)
[RSL 03]
Rauch, J. – Šimůnek, M. – Lín, V.: Mining for Patterns Based on Contingency Tables by
KL-Miner – First Experience. In: LIN, Tsau Young, HU, Xiaohua, OHSUGA, Setsuo,
LIAU, C. J. (ed.). Data mining – Foundations and New Directions in Data Mining. Melbourne: IEEE Computer Society, 2003, pp. 156–163.
[RSC 03]
Rauch, J. – Strossa, P. – Černý, Z.: Reporting Data Mining Result in Natural Language.
In: LIN, Tsau Young (ed.). Foundations and New Directions in Data Mining: Workshop
Notes. Melbourne: IEEE Computer Society, 2003, pp. 164–171.
[Si 03]
Šimůnek, M.: Academic KDD Project LISp-Miner. In: ABRAHAM, A., FRANKE, K.,
KOPPEN, K. (ed.). Advances in Soft Computing – Intelligent Systems Desing and Applications. Heidelberg: Springer-Verlag, 2003, pp. 263–272. ISBN 3-540-40426-0.
[SSR 04]
Svátek, V. – Štochl, J. – Rauch, J.: Matching Data Mining Methods with MetaData and
Problem Descriptions in Recommender Systems. In: SNÁŠEL, Václav (ed.). Znalosti 2004
– poster proceedings. Ostrava: VŠB TU, 2004, pp. 65–68.
[SR 02]
Strossa, P. – Rauch, J.: Association Rules in STULONG and Natural Language. In: BERKA,
Petr (ed.). ECML/PKDD-2002 Workshop Proceedings: Discovery Challenge Workshop
Notes, Report B-2002-8. Helsinki: Universitas Helsingiensis, 2002. ISBN 952-10-06390. ISSN 1458-4786.
[SR 03]
Strossa, P. – Rauch, J.: Converting Association Rules into Natural Language. In:
KLOPOTEK, M. A., WIERZCHON, S. T., TROJANOWSKI, K. (ed.). IIPWM'03. Berlin:
Springer, 2003, pp. 383–392. ISBN 3-540-00843-8.
[So 03]
Štochl, J.: Data mining in catheterization database. In: SVÁTEK, Vojtěch (ed.). Znalosti
2003. Ostrava: TU Ostrava, 2003, pp. 192–201. ISBN 80-248-0229-5. (In Czech)
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LISp-Miner project: http://lispminer.vse.cz
[St 04]
Strossa, P.: AR2NL/STULONG: an Experiment with a Simple Natural Language Model
for Formulating Association Rules. In: SNÁŠEL, Václav (ed.). Znalosti 2004. Ostrava:
VŠB TU, 2004, pp. 210–217. ISBN 80-248-0456-5.
[Ze 96]
Zembowicz, R. – Zytkow, J.: From Contingency Tables to Various Forms of Knowledge
in Databases. in Fayyad, U. M. et al.: Advances in Knowledge Discovery and Data Mining.
AAAI Press/ The MIT Press, 1996. pp. 329–349.
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