Attribute exploration with background knowledge

Attribute Exploration with Background Knowledge

Attribute Exploration with Fuzzy Attributes and Background Knowledge

Attribute exploration is a formal concept analytical tool for knowledge discovery by interactive determination of the implications holding between a given set of attributes. The corresponding algorithm queries the user in an efficient way about the implications between the attributes. The result of the exploration process is a representative set of examples for the entire theory and a set of im...

Attribute Exploration with Background Implications and Exceptions

Implications between attributes can represent knowledge about objects in a speciied context. This knowledge representation is especially useful when it is not possible to list all speciied objects. Attribute exploration is a tool of formal concept analysis that supports the acquisition of this knowledge. For a speciied context this interactive procedure determines a minimal list of valid implic...

Sidec: an Attribute{based Discovery System with Background Knowledge

The discovery of knowledge in databases is currently a very active research area. Many discovery systems adapt traditional attribute{based learners for the extraction of patterns. They have been, however, restricted by their inability to incorporate background knowledge into the learning process. In this paper an attribute{based learning algorithm, called SIDEC, which can incorporate a restrict...

Abstract Attribute Exploration with Partial Object Descriptions

Attribute exploration has been investigated in several studies, with particular emphasis on the algorithmic aspects of this knowledge acquisition method. In its basic version the method itself is rather simple and transparent. But when background knowledge and partially described counterexamples are admitted, it gets more difficult. Here we discuss this case in an abstract, somewhat “axiomatic”...