A Data Mining Tool Using An Intelligent Processing System with a Clustering Application

  • A. M. S. Zalzala
  • A. Al-Zain
  • I. Sarafis
Conference paper


This paper presents DIPS, a database using an intelligent processing system. DIPS is a generic data mining tool for use with real-world applications. The tool is developed in Java and has access to an Oracle server for data storage. A Control GUI facilitates data manipulation, and the tool incorporates a set of algorithms for general data mining and clustering applications including e.g. neural networks and evolutionary computation techniques. Case studies are reported incorporating a rule-based genetic clustering algorithm in experimental and real-world applications.


Data Mining Tool Data Mining Task Oracle Database Cluster Application Java Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Fayyad, U., G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, Advances in knowledge discovery and data mining, A A AI Press/The MIT Press, 1996.Google Scholar
  2. 2.
    Han, J. and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufman Publishers, 2000.Google Scholar
  3. 3.
    Flanagan, D., Java in a Nutshell, O’Reilly, 1999.Google Scholar
  4. 4.
    Oracle Corp., Enterprise DBA Architecture and Administration production 1.0, August 1999.Google Scholar
  5. 5.
    Quatrani, T., Visual Modeling With Rational Rose 2000 and UML, Addison-Wesley, 1998.Google Scholar
  6. 6.
    E. Bonsma, M. Shackleton and R. Shipman, Eos — an evolutionary and ecosystem research platform, BT Technology Journal, 18(14):24–31, 2000.CrossRefGoogle Scholar
  7. 7.
    I Sarafis, AMS Zalzala and P W Trinder, A Genetic Rule-Based Data Clustering Toolkit, In Proc World Congress on Computational Intelligence, May 2002 (to appear).Google Scholar
  8. 8.
    Stonebraker, M., Frew, J., Gardels, K., and Meredith, J. 1993, The Sequoia 2000 Storage Benchmark, In Proc. ACM-SIGMOD International Conference on Management of Data, pp. 2–11, Washington, D.C., May 1993.Google Scholar
  9. 9.
    S. Guha, R. Rastogi, and K. Shim, CURE: An efficient clustering algorithm for large databases, In Proceedings of ACMSIGMOD International Conference on Management of Data, pages 73–84, New York, 1998.Google Scholar
  10. 10.
    Tian Zhang, Raghu Ramakrishnan, and Miron Livny, BIRCH: An Efficient Data Clustering Method for Very Large Databases, In Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, pages 103–114, Montreal, Canada, 1996.Google Scholar

Copyright information

© Springer-Verlag London 2002

Authors and Affiliations

  1. 1.Department of Computing & Electrical EngineeringHeriot-Watt UniversityEdinburgh EHUK

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