Applications of fuzzy and rough set theory in data mining

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations


The explosion of very large databases has created extraordinary opportunities for monitoring, analyzing and predicting global economical, geographical, demographic, medical, political, and other processes in the world. Statistical analysis and data mining techniques have emerged for these purposes. Data mining is the process of discovering previously unknown but potentially useful patterns, rules, or associations from huge quantity of data. Data mining can be performed on different data repositories such as relational databases, data warehouses, transactional databases, sequence databases, spatial databases, spatio-temporal databases, and text databases, etc. Typically, data mining functionalities can be classified into two categories: descriptive and predictive. Descriptive mining tasks aim at characterizing the general properties of the data in the databases, while predictive mining tasks perform inherence on the current data in order to make prediction in future.

Original languageEnglish (US)
Title of host publicationMethods and Supporting Technologies for Data Analysis
EditorsDanuta Zakrzewska, Liliana Byczkowska-Lipinska, Ernestina Menasalvas
Number of pages43
Publication statusPublished - Aug 6 2009

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X


ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Li, D., & Deogun, J. S. (2009). Applications of fuzzy and rough set theory in data mining. In D. Zakrzewska, L. Byczkowska-Lipinska, & E. Menasalvas (Eds.), Methods and Supporting Technologies for Data Analysis (pp. 71-113). (Studies in Computational Intelligence; Vol. 225).