Using rough sets as tools for knowledge discovery
Ning Shan, Wojciech Ziarko, Howard J. Hamilton, Nick J. Cercone · 1995
An attribute-oriented rough set method for knowledge discovery in databases is described. The method is based on information generaliza-tion, which examines the data at various levels of abstraction, followed by the discovery, anal-ysis and simplification of significant data rela-tionships. First, an attribute-oriented concept tree ascension technique is applied to generalize the information; this step substantially reduces the overall computational cost. Then rough set techniques are applied to the generalized infor-mation system to derive rules. The rules repre-sent data dependencies occurring in the database. We focus on discovering hidden patterns in the database rather than statistical summaries.