Semantic analytical reports: A framework for post-processing data mining results

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5722 LNAI, Issue , 2009, Pages 88-98

  Kliegr, Tomáš ^a   Ralbovský, Martin ^a   Svátek, Vojtěch ^a   Šimůnek, Milan ^a   Jirkovský, Vojtěch ^b   Nemrava, Jan ^a   Zemánek, Jan ^a  

^a UNIVERSITY OF ECONOMICS   (Czech Republic)

^b CZECH TECHNICAL UNIVERSITY IN PRAGUE   (Czech Republic)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYTICAL REPORTS; ASSOCIATION RULE MINING; BACKGROUND KNOWLEDGE; CONTENT MANAGEMENT SYSTEM; FINANCIAL DATA; GENERALIZED ASSOCIATION RULES; GENERIC DATA; POST PROCESSING; PROTOTYPE IMPLEMENTATIONS; SEMANTIC KNOWLEDGE; SEMANTIC WEB TECHNOLOGY; TOPIC MAPS;

ASSOCIATION RULES; ASSOCIATIVE PROCESSING; DATA MINING; INTELLIGENT SYSTEMS; KNOWLEDGE BASED SYSTEMS; ONTOLOGY; SEMANTIC WEB; SOFTWARE PROTOTYPING;

SEMANTICS;

EID: 70349884042     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04125-9_12     Document Type: Conference Paper

References (16)

1. W3C: XSL Transformation (1999), http://www.w3.org/TR/xslt
2. DMG: PMML 3.2 Specification, http://www.dmg.org/pmml-v3-2.html
3. Bernstein, A., Provost, F., Hill, S.: Toward intelligent assistance for a data mining process: An ontology-based approach for cost-sensitive classification. IEEE Trans. on Knowl. and Data Eng. 17(4), 503-518 (2005)
4. Cannataro, M., Comito, C.: A data mining ontology for grid programming. In: Proceedings of the 1st Int. Workshop on Semantics in Peer-to-Peer and Grid Computing (SemPGrid 2003), pp. 113-134 (2003)
5. H́ajek, P., Havŕanek, T.: Mechanizing Hypothesis Formation (Mathematical Foundations for a General Theory). Springer, Heidelberg (1978)
6. Kov́ac, M., Kuchar, T., Kuzmin, A., Ralbovsḱy, M.: Ferda, New Visual Environment for Data Mining. In: Znalosti 2006, Czech Rep., pp. 118-129 (2006) (in Czech)
7. Ralbovsḱy, M., Kucha¡r, T.: Using Disjunctions in Association Mining. In: Perner, P. (ed.) ICDM 2007. LNCS (LNAI), vol.4597, pp. 339-351. Springer, Heidelberg (2007)
8. Rauch, J.: Logic of Association Rules. Applied Intelligence 22, 9-28 (2005)
9. Rauch, J., Simunek, M.: Dealing with Background Knowledge in the SEWEBAR Project. In: ECML/PKDD Workshop: Prior Conceptual Knowledge in Machine Learning and Data Mining, Warszaw 2007, pp. 97-108 (2007)
10. Rauch, J., Simunek,M.: LAREDAM Considerations on System of Local Analytical Reports from Data Mining. Toronto 20.05.2008 - 23.05.2008. In: Foundations of Intelligent Systems, pp. 143-149. Springer, Berlin (2008)
11. Rauch, J., Simunek, M.: Semantic Web Presentation of Analytical Reports from Data Mining - Preliminary Considerations. In: Web Intelligence 2007, pp. 3-7. IEEE Computer Society, Los Alamitos (2007)
12. Sv́atek, V., Rauch, J., Ralbovsḱy, M.: Ontology-Enhanced Association Mining. In: Ackermann, M., Berendt, B., Grobelnik, M., Hotho, A., Mladeni¡c, D., Semeraro, G., Spiliopoulou, M., Stumme, G., Sv́atek, V., van Someren, M., et al. (eds.) EWMF 2005 and KDO 2005. LNCS (LNAI), vol.4289, pp. 163-179. Springer, Heidelberg (2006)
13. Suyama, A., Yamaguchi, T.: Specifying and Learning Inductive Learning Systems using Ontologies. In: AAAI 1998 Workshop on the Methodology of Applying Machine Learning, pp. 29-36 (1998)
14. Workshop Notes on Discovery Challenge (workshop at PKDD 1999), Prague, Czech Rep. (August 1999)
15. Zeman, M., Ralbovsḱy, M., Sv́atek, V., Rauch, J.: Ontology-Driven Data Preparation for Association Mining. In: Znalosti 2009, Czech Republic, pp. 270-283 (2009)
16. ¡Źakov́a, M., K¡remen, P., ¡Zelezń y, F., Lavra¡c, N.: Planning to Learn with a Knowledge Discovery Ontology. In: Planning to Learn Workshop at ICML 2008 [CDROM], pp. 29-34. Omnipress, Madison (2008)