AN ARCHITECTURE FOR COLLABORATIVE DATA MINING

Francisco Correia, Rui Camacho, João Correia Lopes

2010

Abstract

Collaborative Data Mining (CDM) develops techniques to solve complex problems of data analysis requiring sets of experts in different domains that may be geographically separate. An important issue in CDM is the sharing of experience among the different experts. In this paper we report on a framework that enables users with different expertise to perform data analysis activities and profit, in a collaborative fashion, from expertise and results of other researchers. The collaborative process is supported by web services that seek for relevant knowledge available among the collaborative web sites. We have successfully designed and deployed a prototype for collaborative Data Mining in domains of Molecular Biology and Chemoinformatics.

References

  1. Blockeel, H. and Moyle, S. (2002). Collaborative data mining needs centralised model evaluation. In Proceedings of the ICML-2002 Workshop on Data Mining Lessons Learned, pages 21-28.
  2. Booth, D. and Liu, C. K. (2007). Web services description language (WSDL) version 2.0 part 0: Primer. Technical Report Second Edition, W3C Recommendation. http://www.w3.org/TR/wsdl20-primer.
  3. CRISP-DM (2007). Cross industry standard process for data mining. http://www.crisp-dm.org/.
  4. Dzeroski, S. (2001). Relational Data Mining. SpringerVerlag New York, Inc., Secaucus, NJ, USA.
  5. Lavrac, N., Motoda, H., Fawcett, T., Holte, R., Langley, P., and Adriaans, P. (2004). Introduction: Lessons learned from data mining applications and collaborative problem solving. Machine Learning, 57(1-2):13- 41.
  6. Mitra, N. and Lafon, Y. (2007). SOAP version 1.2 part 0: Primer. Technical Report Second Edition, W3C Recommendation. http://www.w3.org/TR/soap12-part0/.
  7. Moller, A. and Schwartzbach, M. I. (2006). An Introduction to XML and Web Technologies. Addison Wesley.
  8. Moyle, S., McKenzie, J., and Jorge, A. M. (2003). Collaboration in a data mining virtual organization. In Data Mining and Decision Support: Integration and Collaboration, The International Series in Engineering and Computer Science, chapter 5, pages 49-62. Springer.
  9. Muggleton, S. and De Raedt, L. (1994). Inductive logic programming: Theory and methods. Journal of Logic Programming, 19/20:629-679.
  10. Papazoglou, M. P. and Georgakopoulos, D. (2003). Serviceoriented computing. Communications of the ACM, 46(10):2528.
  11. Srinivasan, A. (2003). The Aleph Manual. Available from http://web.comlab.ox.ac.uk/oucl/research/areas/ machlearn/Aleph.
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Paper Citation


in Harvard Style

Correia F., Camacho R. and Lopes J. (2010). AN ARCHITECTURE FOR COLLABORATIVE DATA MINING . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010) ISBN 978-989-8425-28-7, pages 467-470. DOI: 10.5220/0003097504670470


in Bibtex Style

@conference{kdir10,
author={Francisco Correia and Rui Camacho and João Correia Lopes},
title={AN ARCHITECTURE FOR COLLABORATIVE DATA MINING},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010)},
year={2010},
pages={467-470},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003097504670470},
isbn={978-989-8425-28-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010)
TI - AN ARCHITECTURE FOR COLLABORATIVE DATA MINING
SN - 978-989-8425-28-7
AU - Correia F.
AU - Camacho R.
AU - Lopes J.
PY - 2010
SP - 467
EP - 470
DO - 10.5220/0003097504670470