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A General Approach to Incorporate Data Quality Matrices Into Data Mining Algorithms by Ian Davidson; Ashish Grover; Ashwin Satyanarayana; Giri K. Tayi is a scholarly article available to read on EtoBox.

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Data quality is a central issue for many information-oriented organizations. Recent advances in the data quality field reflect the view that a database is the product of a manufacturing process. While routine errors, such as non-existent zip codes, can be detected and corrected using traditional data cleansing tools, many errors systemic to the manufacturing process cannot be addressed. Therefore, the product of the data manufacturing process is an imprecise recording of information about the entities of interest (i.e. customers, transactions or assets). In this way, the database is only one (flawed) version of the entities it is supposed to represent. Quality assurance systems such as Motorola's Six-Sigma and other continuous improvement methods document the data manufacturing process's shortcomings. A widespread method of documentation is quality matrices. In this paper, we explore the use of the readily available data quality matrices for the data mining classification task. We first illustrate that if we do not factor in these quality matrices, then our results for prediction are sub-optimal. We then suggest a general-purpose ensemble approach that perturbs the data according t

Author

Ian Davidson; Ashish Grover; Ashwin Satyanarayana; Giri K. Tayi

Publisher

ACM

Published

2004

Language

EN