Welcome to the InfoSci Platform
Reference Hub1
Data Reduction with Rough Sets

Data Reduction with Rough Sets

Richard Jensen
Copyright: © 2009 |Pages: 5
ISBN13: 9781605660103|ISBN10: 1605660108|EISBN13: 9781605660110
DOI: 10.4018/978-1-60566-010-3.ch087
Cite Chapter Cite Chapter

MLA

Jensen, Richard. "Data Reduction with Rough Sets." Encyclopedia of Data Warehousing and Mining, Second Edition, edited by John Wang, IGI Global, 2009, pp. 556-560. https://doi.org/10.4018/978-1-60566-010-3.ch087

APA

Jensen, R. (2009). Data Reduction with Rough Sets. In J. Wang (Ed.), Encyclopedia of Data Warehousing and Mining, Second Edition (pp. 556-560). IGI Global. https://doi.org/10.4018/978-1-60566-010-3.ch087

Chicago

Jensen, Richard. "Data Reduction with Rough Sets." In Encyclopedia of Data Warehousing and Mining, Second Edition, edited by John Wang, 556-560. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-010-3.ch087

Export Reference

Mendeley
Favorite

Abstract

Data reduction is an important step in knowledge discovery from data. The high dimensionality of databases can be reduced using suitable techniques, depending on the requirements of the data mining processes. These techniques fall in to one of the following categories: those that transform the underlying meaning of the data features and those that are semantics-preserving. Feature selection (FS) methods belong to the latter category, where a smaller set of the original features is chosen based on a subset evaluation function. The process aims to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. In knowledge discovery, feature selection methods are particularly desirable as they facilitate the interpretability of the resulting knowledge. For this, rough set theory has been successfully used as a tool that enables the discovery of data dependencies and the reduction of the number of features contained in a dataset using the data alone, while requiring no additional information.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.