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Wednesday 22 May 2013

Dissertation Series - Barriers to successful Data Mining - Missing data


as more data is collected, the higher the likelihood of missing data” (Brown et al, 2003 p.611) and as such the treatment of the missing data must be taken into consideration and the effect that such treatment will have on the end result “missing data may be replaced with estimates.  This and other approaches to handling missing data can lead to invalid results in the data mining step” (Dunham, 2003 p.15).
Missing data may be as a result of individuals refusing to provide certain data, Brown and Kros (2003 p.612) refer to an example of a medical environment where “respondents may find certain survey questions offensive or they may be personally sensitive to certain questions”.

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