Abstract
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.
Keywords
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
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.
Han, J. and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufman Publishers, 2000.
Flanagan, D., Java in a Nutshell, O’Reilly, 1999.
Oracle Corp., Enterprise DBA Architecture and Administration production 1.0, August 1999.
Quatrani, T., Visual Modeling With Rational Rose 2000 and UML, Addison-Wesley, 1998.
E. Bonsma, M. Shackleton and R. Shipman, Eos — an evolutionary and ecosystem research platform, BT Technology Journal, 18(14):24–31, 2000.
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).
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.
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.
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag London
About this paper
Cite this paper
Zalzala, A.M.S., Al-Zain, A., Sarafis, I. (2002). A Data Mining Tool Using An Intelligent Processing System with a Clustering Application. In: Parmee, I.C. (eds) Adaptive Computing in Design and Manufacture V. Springer, London. https://doi.org/10.1007/978-0-85729-345-9_28
Download citation
DOI: https://doi.org/10.1007/978-0-85729-345-9_28
Publisher Name: Springer, London
Print ISBN: 978-1-85233-605-9
Online ISBN: 978-0-85729-345-9
eBook Packages: Springer Book Archive