Performance Implications of Knowledge Discovery Techniques in Databases

Performance Implications of Knowledge Discovery Techniques in Databases

Balaji Rajagopalan (Oakland University, USA) and Ravindra Krovi (University of Akron, USA)
Copyright: © 2003 |Pages: 22
DOI: 10.4018/978-1-59140-063-9.ch009
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Abstract

This chapter introduces knowledge discovery techniques as a means of identifying critical trends and patterns for business decision support. It suggests that effective implementation of these techniques requires a careful assessment of the various data mining tools and algorithms available. Both statistical and machine-learning based algorithms have been widely applied to discover knowledge from data. In this chapter we describe some of these algorithms and investigate their relative performance for classification problems. Simulation based results support the proposition that machine-learning algorithms outperform their statistical counterparts, albeit only under certain conditions. Further, the authors hope that the discussion on performance related issues will foster a better understanding of the application and appropriateness of knowledge discovery techniques.

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