Titlebook: Data Mining, Rough Sets and Granular Computing; Tsau Young Lin,Yiyu Y. Yao,Lotfi A. Zadeh Book 2002 Springer-Verlag Berlin Heidelberg 2002

主講人

objection

Afterword: Some Notes on Radical Teachingo observability problems within a classical boolean structure or a fuzzy context. In doing so, it is shown how ideas arising in Fuzzy Sets theory can become fine tools to handle most of the granulating problems. In the last section we study the observability of the label “probable” viewed as a fuzzy set.

CHASE

endoscopy

A Query-Driven Interesting Rule Discovery Using Associations and Spanning Operationsttributes. The combination of two operations keeps both confidence and supportiveness measures together, and differenciation of query views enables us to produce the desired level of interestingness and relevancy.

Vasoconstrictor

Small-Intestine

FRONT

An Interactive Visualization System for Mining Association Rulesd visualizing numerical association rules is proposed. Our experimental result on a census data set shows that the AViz system is useful and helpful for discovering and visualizing numerical association rules.

脫離

人類學(xué)家

Validation of Concept Representation with Rule Induction and Linguistic Variableswhich suggests that combination of rule induction and attribute-oriented generalization can be used to validate concept hiearchy. Interestingly, fuzzy linguistic variables play an important role in solving these problems.

CARK

Granular Computing Using Information Tables granules. Objects in each granule share the same or similar description in terms of their attribute values. Studies along this line have been carried out in the theories of rough sets and databases. Within the proposed model, this paper reviews the pertinent existing results and presents their generalizations and applications.