Data preprocessing: Difference between revisions

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Revision as of 13:36, 3 January 2012

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{{Context|date=October 2009}}

'''Data pre-processing''' is an often neglected but important step in the data mining process. The phrase [[GIGO|"Garbagegarbage Inin, Garbagegarbage Outout"]] is particularly applicable to [[data mining]] and [[machine learning]] projects. Data -gathering methods are often loosely controlled, resulting in out-of-range values (e.g., Income: -100−100), impossible data combinations (e.g., Gender: Male, Pregnant: Yes), [[missing values]], etc. Analyzing data that has not been carefully screened for such problems can produce misleading results. Thus, the representation and quality of data is first and foremost before running an analysis.<ref>Pyle, D., 1999. ''Data Preparation for Data Mining.'' Morgan Kaufmann Publishers, [[Los Altos, California]].</ref>

If there is much irrelevant and redundant information present or noisy and unreliable data, then [[knowledge discovery]] during the training phase is more difficult. Data preparation and filtering steps can take considerable amount of processing time. Data pre-processing includes [[Data cleaning|cleaning]], normalization, transformation, [[feature extraction]] and selection, etc. The product of data pre-processing is the final [[training set]]. Kotsiantis et al. (2006) present a well-known algorithm for each step of data pre-processing.<ref>S. Kotsiantis, D. Kanellopoulos, P. Pintelas, "Data Preprocessing for Supervised Leaning", ''International Journal of Computer Science'', 2006, Vol 1 N. 2, pp 111-117111–117.</ref>

==References==