Mining quantitative data based on tolerance rough set model

Authors:
Hsuan-Shih Lee;Pei-Di Shen;Wen-Li Chyr;Wei-Kuo Tseng
Affiliations:
Department of Shipping and Transportation Management, National Taiwan Ocean University, Keelung, Taiwan;Department of Information Management, Ming Chung University, Taipei, Taiwan;Graduate School of Management, Ming Chung University, Taipei, Taiwan;Department of Shipping and Transportation Management, National Taiwan Ocean University, Keelung, Taiwan
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Year:
2005

Citing 6
Cited 0

Rough classification

International Journal of Man-Machine Studies
Rough sets: a new approach to vagueness

Fuzzy logic for the management of uncertainty
Further remarks on the relation between rough and fuzzy sets

Fuzzy Sets and Systems
Rough membership functions

Advances in the Dempster-Shafer theory of evidence
Rough sets and fuzzy sets

Fuzzy Sets and Systems
A fuzziness measure of rough sets

IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Rough set theory has been widely used in knowledge acquisition. However, In conventional application of rough set to numeric data, data must be pre-classified. In this paper, a different approach is introduced to deal with numeric data. We develop a mining algorithm based on fuzzy sets and tolerance rough set model, which offers a way of relating data in their semantics.