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WIREs Data Mining Knowl Discov
Impact Factor: 2.541

Information enhancement for data mining

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Abstract Information enhancement techniques are desired in many areas such as data mining, machine learning, business intelligence, and web data analysis. Information enhancement mainly includes the following topics: data cleaning, data preparation and transformation, missing values imputation, feature and instance selection, feature construction, treatment of noisy and inconsistent data, data integration, data collection and housing, information enhancement, web data availability, web data capture and representation, and the others. It is impossible to outline all the research topics in a single paper. In this study, we discuss the information enhancement for data mining with existing missing data imputation techniques. We first review the current research on imputing missing values, and then experimentally evaluate the techniques and demonstrate the efficiency of missing data imputation techniques to enhance information in the process of pattern discovery from datasets with missing values. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 284–295 DOI: 10.1002/widm.21 This article is categorized under: Fundamental Concepts of Data and Knowledge > Data Concepts

Patterns in relational datasets: (a) dataset without missing values, (b) missing in independent variables, (c) missing in dependent variables, and (d) missing in both independent and dependent variables. In each case, rows correspond to instance and columns correspond to variables including independent variables (Fi) and dependent variables (C), ‘?’ means missing values.

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