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

中止

Finding Unexpected Patterns in Datarizing the . of patterns based on prior background knowledge in the form of beliefs. Based on this characterization of unexpectedness we present an algorithm, ZoomUR, for discovering unexpected patterns in data.

Gum-Disease

LUCY

Bowel problems — too much or too littleand experiments show that the computation is fast and is a promising approach. Experiments show about 15–20 time faster; theoretical analysis indicates that on the counting the support step, which is the major step, it is at least 32 (wordsize) time faster.

軟弱

Afterword: Some Notes on Radical Teachingithms are provided. The proposed rule induction method was evaluated on medical databases, the experimental results of which show that induced rules correctly represented experts’ knowledge and several interesting patterns were discovered.

憎惡

John Malcolm Dowling,Chin-Fang Yapcepts and conceptual structures are distinguished) and to logical precision both of which represent a systematic way to control the granularity and reduce the complexity of the conceptual structure. Applications in conceptual data analysis and representation of conceptual knowledge are discussed.

CLIFF

Book 2002- ticularly true in the realm of e-commerce, where data mining is moving from a "nice-to-have" to a "must-have" status. In a different though related context, a new computing methodology called granular computing is emerging as a powerful tool for the conception, analysis and design of information/i

空中

Data Mining Using Granular Computing: Fast Algorithms for Finding Association Rulesand experiments show that the computation is fast and is a promising approach. Experiments show about 15–20 time faster; theoretical analysis indicates that on the counting the support step, which is the major step, it is at least 32 (wordsize) time faster.