Database Sampling for Data Mining

Database Sampling for Data Mining

Patricia E.N. Lutu (University of Pretoria, South Africa)
Copyright: © 2009 |Pages: 6
DOI: 10.4018/978-1-60566-010-3.ch095
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In data mining, sampling may be used as a technique for reducing the amount of data presented to a data mining algorithm. Other strategies for data reduction include dimension reduction, data compression, and discretisation. For sampling, the aim is to draw, from a database, a random sample, which has the same characteristics as the original database. This chapter looks at the sampling methods that are traditionally available from the area of statistics, how these methods have been adapted to database sampling in general, and database sampling for data mining in particular.
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There are a number of key issues to be considered before obtaining a suitable random sample for a data mining task. It is essential to understand the strengths and weaknesses of each sampling method. It is also essential to understand which sampling methods are more suitable to the type of data to be processed and the data mining algorithm to be employed. For research purposes, we need to look at a variety of sampling methods used by statisticians, and attempt to adapt them to sampling for data mining.

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