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The Pitfalls of Knowledge Discovery in Databases and Data Mining

The Pitfalls of Knowledge Discovery in Databases and Data Mining
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Author(s): John Wang (Montclair State University, USA) and Alan Oppenheim (Montclair State University, USA)
Copyright: 2003
Pages: 19
Source title: Data Mining: Opportunities and Challenges
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-59140-051-6.ch009

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Abstract

Although Data Mining (DM) may often seem a highly effective tool for companies to be using in their business endeavors, there are a number of pitfalls and/or barriers that may impede these firms from properly budgeting for DM projects in the short term. This chapter indicates that the pitfalls of DM can be categorized into several distinct categories. We explore the issues of accessibility and usability, affordability and efficiency, scalability and adaptability, systematic patterns vs. sample-specific patterns, explanatory factors vs. random variables, segmentation vs. sampling, accuracy and cohesiveness, and standardization and verification. Finally, we present the technical challenges regarding the pitfalls of DM.

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