Skip to main content
SpringerLink
Log in

Data mining ontology development for high user usability

  • Web Information Mining and Retrieval
  • Published:
Wuhan University Journal of Natural Sciences

Abstract

This paper mainly introduces the development and implementation of the user-centered data mining service ontology on Universal Knowledge Grid (UKG), UKG is an ontology-based grid architecture model to build large-scale distributed knowledge discovery system on the grid. The data mining ontology services are the main service offering by UKG. It can meet the user requirements of knowledge discovery in different domains and different hierarchies and make the system exoteric, extensible and high usable. A data mining solution for money laundering is introduced.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Gruber T R. A Translation Approach to Portable Ontology Specifications.Knowledge Acquisition. 1993.5 (2): 199–220.

    Article  Google Scholar 

  2. Li-Yu-hua, Lu Zheng-ding. Ontology-Based Universal Knowledge Grid: Enabling Knowledge Discovery and Integration on the Grid.Proceedings 2004 IEEE International Conference on Services Computing, Shanghai, China, September 2004, 557–560.

  3. Klett F. The Impact of User-Centered Design Concepts in Virtual Learning Environments.Proceedings of the Fifth International Conference on Information Technology Based Higher Education and Training. Instanbut, Turkey, June 2004. 222–226.

  4. Grossman R. Supporting the Data Mining Process with Next Generation Data Mining Systems.http://www.lac.uic. edu/≈grossman/paper/esj-98.htm.98 December 1998.

  5. Piatetsky-Shapiro G. Knowledge Discovery in Database: 10 Years after.SIGKDD Explorations. 2000,1(2): 59–61.

    Article  Google Scholar 

  6. Lu H. Seamless Integration of DM with DBMS and Application,http://www.cs.ust.hk/luhj/ps/pakdd01.pdf, June 2001.

  7. Han Jia-wei, Data Mining-Current Status and Research Directions,http://db.cs.sfu.cu/sections/publications/slides/slides.htm, August 2001.

  8. Haseltine E. Invited Talks: User Centered Design for KDD.http://www.acm.org/sigs/sigkdd/kdd2004/program/invited_talks.html, August 2004.

  9. KDnuggets. DDnuggets Polls: Data Mining Applications.www.kdnuggets.com/polls/2004/data_mining_applications_industries.htm. August 2004.

  10. Bohanec M, Moyle S. Wettschereck D,et al. A Software Architecture for Data Preprocessing Using Data Mining and Decision Support Models.Workshop Integration Aspects of Data Mining. Decision Support and Meta Learning. Freiburg. Germany, September 2001. 13–24.

  11. McGuinness D L. Harmelen F V. OWI. Web Ontology Language Overview.http://www.w3.org/2004/OWL/, July 2004.

  12. Chen Hsin-chun, Chung Wing-yan, Xu J,et al. Crime Data Mining: A General Framework and Some Examples.IEEE Computer, 2004,37(4): 50–56.

    Google Scholar 

  13. Hauck R V. Chen H. Coplink: A case of Intelligent Analysis and Knowledge Management.Proceedings of the International Conference on Information Systems, Charlotte. North Carolina, USA, December 1999. 15–28.

  14. Agrawal R, Gehrke J, Gunopulos D,et al. Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications.International Conference on Management of Data Proceedings of the 1998 ACMSIGMOD International Conference on Management of Data. New York: ACM Press, 1998. 95–105.

    Google Scholar 

  15. Xu J, Chen H. Fighting Organized Crime: Using Shortest-Path Algorithms to Identify Associations in Criminal Networks.Decision Support Systems, 2004,38(3): 473–487.

    Article  Google Scholar 

  16. Mehta M. Agawal R, Rissanen J. SLIQ: A Fast Scalable Classifier for Data Minig.In Proc of the 5th International Conference on Extending Database Technology (EDBT). Avignon, France, March 1996. 18–32.

  17. Jin W, Tung A K. H. Han Jia-wei, Mining Top-n Local Outliers in Large Databases.In Proceedings of 7th. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, CA, USA, August 2001. 293–298.

  18. Kaya M, Alhajj R. Multi-Objective Genetic Algorithm Based Approach for Optimizing Fuzzy Sequential Patterns.Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2004). Boca Raton, FL, USA, November 2004, 396–400.

  19. Han Jia-wei, Kamber M.Data Mining Concepts and Techniques Beijing:Higher Education Press, 2001. 319–321.

    Google Scholar 

  20. Congiusta A, Pugliese A, Talia D,et al. Designing Grid Services for Distributed Knowledge Discovery.Web Intelligence and Agent Systems, 2003,1(2): 91–104.

    Google Scholar 

  21. Cannataro M, Comito C. A Data Mining Ontology for Grid Programming.1st International Workshop on Semantics in Peer-to-Peer and Grid Computing, in Conjunction with WWW2003 Budapest, Hungary, May 2003. 113–134.

Download references

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Huazhong University of Science and Technology, 430074, Wuhan Hubei, China

    Li Yu-hua, Lu Zheng-ding, Sun Xiao-lin, Wen Kun-mei & Li Rui-xuan

Authors
  1. Li Yu-hua
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lu Zheng-ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sun Xiao-lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wen Kun-mei
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Li Rui-xuan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lu Zheng-ding.

Additional information

Foundation item: Supported by the National Natural Science Foundation of China (60403027), the Natioanl Key Technologies R&D Program of China during the 10th Five-Year Plan Period (2002BA103A04, 2001BA102A06-11).

Biography: Li Yu-hua (1968-), female, Ph.D. candidate, Associate professor, research direction: data mining, knowledge management. semantic Web and ontology and artificial intelligence

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yu-hua, L., Zheng-ding, L., Xiao-lin, S. et al. Data mining ontology development for high user usability. Wuhan Univ. J. Nat. Sci. 11, 51–56 (2006). https://doi.org/10.1007/BF02831703

Download citation

  • Received:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02831703

Key words

CLC number

Search

Navigation