Title :
Rule discovery based on rough set theory
Author :
Yang, Yanyi ; Chiam, T.C.
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore
Abstract :
The volume of data being generated nowadays is increasingly large. How to extract useful information from such data collections is an important issue. A promising technique is rough set theory, a new mathematical approach to data analysis based on the classification of objects of interest into similarity classes which are indiscernible with respect to some features. This theory offers two fundamental concepts: reduct and core. In this paper, some basic ideas of rough set theory are first presented, followed by a new heuristic approach for rule induction that is outlined using an illustrative example. Some experimental results are also given.
Keywords :
data analysis; data mining; database theory; deductive databases; heuristic programming; inference mechanisms; pattern classification; rough set theory; very large databases; core; data analysis; data mining; heuristic approach; knowledge discovery; large data collections; object classification; reduct; rough set theory; rule discovery; rule induction; similarity classes; useful information extraction; Data analysis; Data engineering; Data mining; Database systems; Fuzzy set theory; Information systems; Knowledge acquisition; Set theory; Uncertainty;
Conference_Titel :
Information Fusion, 2000. FUSION 2000. Proceedings of the Third International Conference on
Conference_Location :
Paris, France
Print_ISBN :
2-7257-0000-0
DOI :
10.1109/IFIC.2000.862688
