Mining Association Rules with Rough Sets

Abstract

Abstract. We say that there is an association between two sets of items when the sets are likely to occur together in transactions. In information retrieval, an association between two keyword sets means that they co-occur in a record or document. In databases, an association is a rule latent in the databases whereby an attribute set can be inferred from another.Generally, the number of associations may be large, so we look for those that are particularly strong. Maximal association rules were introduced by [3,4], and there is only one maximal association.Rough set theory has been used successfully for data mining. By using this theory, rules that are similar to maximal associations can be found. However, we show that the rough set approach to discovering knowledge is much simpler than the maximal association method.

Discovering attributes dependency for categorical data set based on soft set theory for better decision making

“…To those notions, it is clear that the pre-requisite of their approach is a transformation of a transactional database (bit-map table) into taxonomy-categorized table. In [14,16,17,18], they are based on a widely used Boolean-valued information from Reuters-21578 [19] a labeled document collection, i.e. a benchmark for text categorization, as follows.…”

“…The rough set theory [1] has been extensively used for discovering attribute dependencies in information system. It also has been applied in the many other domains; association rule mining [10,14,15,16,17,18], database management [21], reverse engineering, data clustering [5,6,7] and a few others. An attribute dependency defines that the value of an attribute is distinctively determined by the values of some other attributes.…”