Premium
Information enhancement for data mining
Author(s) -
Zhang Shichao
Publication year - 2011
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.21
Subject(s) - missing data , imputation (statistics) , computer science , data mining , feature selection , external data representation , knowledge extraction , data science , information retrieval , machine learning , artificial intelligence
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