Towards MKDA: A knowledge discovery assistant for researches in medicine

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

Volumn 4733 LNAI, Issue , 2007, Pages 773-780

  Cannella, Vincenzo ^a   Russo, Giuseppe ^a   Peri, Daniele ^a   Pirrone, Roberto ^a   Ardizzone, Edoardo ^a  

^a UNIVERSITY OF PALERMO   (Italy)

Author keywords

Data mining; Knowledge discovery assistant; Knowledge discovery in databases; Workflow generation

Indexed keywords

DATABASE SYSTEMS; FORMAL LANGUAGES; KNOWLEDGE ENGINEERING; WEB SERVICES;

DATA ANALYSTS; KNOWLEDGE DISCOVERY ASSISTANT; WEB BASED SYSTEMS; WORKFLOW GENERATION;

DATA MINING;

EID: 38049121677     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-74782-6_68     Document Type: Conference Paper

References (14)

1. Fayyad, U.M., Haussler, D., Smith, P., Uthurusamy, R.: Advance in Knowledge Discovery and Data Mining. In: AKDDM, AAAI/MIT Press (1996)
2. Aleven, V., Koedinger, K.R.: An effective metacognitive strategy: Learning by doing and explaining with a computer-based cognitive tutor. Cognitice Science 26, 147-179 (2002)
3. Craw, S., Sleeman, D., Graner, N., Rissakis, M., Sharma, S.: Consultant: Providing Advice for the Machine Learning Toolbox. In: Research and Development in Expert Systems IX: Proc. Expert Systems, 12th Ann. Technical Conf. British Computer Soc. Specialist Group on Expert Systems, pp. 5-23 (1992)
4. Brazdil, P.B., Scares, C., Da Costa, J.P.: Ranking learning algorithms: using IBL and meta-learning on accuracy and time results Machine Learning. 50(3), 251-277 (2003)
5. Kalousis, A., Hilario, M.: Model selection via meta-learning: a comparative study. In: ICTAI'O. Ictai 12th IEEE International Conference on Tools with Artificial Intelligence, p. 206. IEEE Computer Society Press, Los Alamitos (2000)
6. Jiawei, Fu, H.Y., Wang, W., Chiang, J., Gong, W., Koperski, K., Li, D., Lu, Y., Rajan, A., Stefanovic, N., Xia, B., Zaiane, O.R.: DBMiner: A System for Mining Knowledge in Large Relational Databases. In: KDD'96. Proc. 1996 Int'l Conf. on Data Mining and Knowledge Discovery, pp. 250-255 (1996)
7. Wirth, R., Shearer, C., Grimmer, U., Reinartz, T., Schlosser, J., Breitner, C., Engels, R., Lindner, G.: Towards Process-Oriented Tool Support for KDD. In: Komorowski, J., Żytkow, J.M. (eds.) PKDD 1997. LNCS, vol. 1263, pp. 55-64. Springer, Heidelberg (1997)
8. Bernstein, A., Provost, F., Hill, S.: Toward Intelligent Assistance for a Data Mining Process: An Ontology-Based Approach for Cost-Sensitive Classification. Proc. of IEEE Transactions on Knowledge and Data Engineering 17(4), 503-518 (2005)
9. Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: YALE: Rapid prototyping for complex data mining tasks. In: KDD 2006. Proc. of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press, New York (2006)
10. Witten, I.H., Frank, E.: Data mining: Practical machine learning tools and techniques with Java implementations. Morgan Kaufmann, San Francisco, CA, USA (2000)
11. (Alice), http://www.alicebot.org/
12. Euler, T.: Publishing Operational Models of Data Mining Case Studies. In: ICDM. Proc. of the Workshop on Data Mining Case Studies at the 5th IEEE International Conference on Data Mining, p. 99. IEEE Computer Society Press, Los Alamitos
13. Carroll, J.J., Dickinson, I., Dollin, C., Reynolds, D., Seaborne, A., Wilkinson, K.: Jena: Implementing the Semantic Web Recommendations. Tech. Rep. HPL-2003-146, HP Laboratories Bristol (2003)
14. Helfin, J. (eds.): Web Ontology Language (OWL): Use Cases and Requirements. W3C Recommendation (February 10, 2004)