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Assessing and Improving the Quality of Knowledge Discovery Data
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Author(s): Herna L. Viktor (University of Pretoria, South Africa) and Niek F. du Plooy (University of Pretoria, South Africa)
Copyright: 2001
Pages: 3
Source title:
Managing Information Technology in a Global Economy
Source Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-930708-07-5.ch098
ISBN13: 9781930708075
EISBN13: 9781466665323
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Abstract
Data quality has a substantial impact on the quality of the results of a Knowledge Discovery from Data (KDD) effort. The poor quality of real-world data, as contained in many large data repositories, poses a serious threat to the future adoption of this new technology. Unfortunately, data quality assessment and improvement are often ignored in many KDD efforts, leading to disappointing results. This paper discusses the use of data mining and data generation techniques, including feature selection, case selection and outlier detection, to assess and improve the quality of the data. In this approach, redundant low quality data are removed from the data repository and new high quality data patterns are dynamically added to the data set. We also point out that data capturing is part of the social practices of office work, and this fact must be taken into account in designing the data capturing processes.
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