7 - Indira Gandhi Centre for Atomic Research

and accuracy of the generated rules. Some suggestions for optimizing the performance of
data mining systems are
• Reducing the size of dataset considered for discovery
• Avoid discovering inconsistent, redundant, trivial knowledge,
• Defining more efficient data mining algorithm and
• Employing high performance systems to improve the discovery process.
Through the application of Knowledge discovery tools, Digital library will evolve as
dynamic landscape for knowledge creation, dissemination and management
References
1. Saso Dzeroski and Nada Lavarac. Relational Data Mining. Berlin:Springer, 2001.ISBN 3-
540-42289-7.
2. Cornelius T. Leondes. Knowledge-Based Systems Techniques and Application (Vol-2). San
Diego: Academic Press, 2000. ISBN 0-12-443877-6.
3. Knowledge Discovery In Databases: Tools and Techniques. Preggy Wright .ACM, 1998.
URL:< http://www.acm.org/crossroads/xrds5-2/kdd.html>
4. C.W.Holsapple. Hand Book on Knowledge Management (Vol 1& 2). Berlin:Springer,
2003. ISBN 81-8128-038-5.
5. Analysis of Data Mining Algorithms. Karuna Pande Joshi. URL<
http://userpages.umbc.edu/~kjoshi1/data-mine/proj_rpt.htm>
6. Jun Wang. A Knowledge Network Constructed by Integrating Classification, Thesaurus,
and Metadata in Digital Library. Intl. Inform. & Libr. Rev. Vol35 Issue 3. 2003, pp383-
397.
7. Lorcan Dempsey, Derek Law and Ian Mowat. Networking and the future of digital
libraries. London: Library Association Publishing, 1997. ISBN 1-85604-241-3.
8. 8.Shu-hsien Liao. Knowledge Management Technologies and Application Literature
review from 1995-2002. Expert Systems with Applications. Vol.25, Issue 2. August 2003,
pp 155-164.
9. Usama Fayyad et.al. From Data Mining to Knowledge Discovery in Databases. American
Association for Artificial Intelligence. URL:< http://www.kdnuggets.com/gpspubs/aimagkdd-overview-1996-Fayyad.pdf>
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