Imprecise Data and the Data Mining Process

Imprecise Data and the Data Mining Process

Marvin L. Brown (Grambling State University, USA) and John F. Kros (East Carolina University, USA)
Copyright: © 2005 |Pages: 6
DOI: 10.4018/978-1-59140-557-3.ch112
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Abstract

Missing or inconsistent data has been a pervasive problem in data analysis since the origin of data collection. The management of missing data in organizations has recently been addressed as more firms implement large-scale enterprise resource planning systems (see Vosburg & Kumar, 2001; Xu et al., 2002). The issue of missing data becomes an even more pervasive dilemma in the knowledge discovery process, in that as more data is collected, the higher the likelihood of missing data becomes.

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