Titlebook: ;

陰謀

G. M. Klump,R. J. Dooling,W. C. Stebbins of one of the frameworks depending on factors such as data structure and pattern representation. It also shows the contribution of settlement of relational data mining in the paradigm of granular computing.

enmesh

打包

驕傲

Association Discovery and Classification Rule Mininghe general granular computing framework for mining relational data. The chapter also shows that the time complexity of generating rules from a granular representation of relational data can be decreased.

滋養(yǎng)

Rough-Granular Computingat is adapted to data stored in a relational databases. The chapter also defines a range of similarity measures for relational data. They are used in the construction of one of the tolerance rough set model components, i.e. the uncertainty function.

contrast-medium

Relation-Based Granulesress richer knowledge about relational data. The chapter also shows that the generation of patterns can be accelerated thanks to using an alternative representation of relational data constructed on the basis of relation-based granules.

Distribution

Compound Approximation Spacesinformation systems and the tolerance rough set model extended to a relational case. The chapter also shows that in constrained compound approximations spaces it is possible to approximate not only a combination of concepts, each of which is defined in one database table, but also the relationships that occur among the concepts.

充足

Geyser

Quantification of Behavioural Processes,This chapter provides an introduction to the fields of relational data mining and granular computing. It also reviews research at the intersection of both fields. Besides, a brief analysis of the problem of constructing a granular computing framework for mining relational data is given.

尊敬

Neuroendocrine Markers of CNS Drug Effects,This chapter provides a general framework for analyzing and processing relational data in a granular computing environment. It introduces compound information systems for relational data. It also extends an attribute-value language for defining relational patterns.