TY - JOUR
T1 - A rough sets based characteristic relation approach for dynamic attribute generalization in data mining
AU - Li, Tianrui
AU - Ruan, Da
AU - Wets, Geert
AU - Song, Jing
AU - Xu, Yang
A2 - Laes, Erik
N1 - Score = 10
PY - 2007/6
Y1 - 2007/6
N2 - Any attribute set in an information system may be evolving in time when new information arrives. Approximations of a concept by rough set theory need updating for data mining or other related tasks. For incremental updating approximations of a concept, methods using the tolerance relation and similarity relation have been previously studied in literature. The characteristic relation-based rough sets approach provides more informative results than the tolerance-and-similarity relation based approach. In this paper, an attribute generalization
and its relation to feature selection and feature extraction are firstly discussed. Then, a new approach for incrementally updating approximations of a concept is presented under the characteristic relation-based rough sets. Finally, the pproach of direct computation of rough set approximations and the proposed approach of dynamic maintenance of rough set approximations are employed for performance comparison. An extensive experimental evaluation on a large soybean database from MLC shows that the proposed approach effectively handles a dynamic attribute generalization in data mining.
AB - Any attribute set in an information system may be evolving in time when new information arrives. Approximations of a concept by rough set theory need updating for data mining or other related tasks. For incremental updating approximations of a concept, methods using the tolerance relation and similarity relation have been previously studied in literature. The characteristic relation-based rough sets approach provides more informative results than the tolerance-and-similarity relation based approach. In this paper, an attribute generalization
and its relation to feature selection and feature extraction are firstly discussed. Then, a new approach for incrementally updating approximations of a concept is presented under the characteristic relation-based rough sets. Finally, the pproach of direct computation of rough set approximations and the proposed approach of dynamic maintenance of rough set approximations are employed for performance comparison. An extensive experimental evaluation on a large soybean database from MLC shows that the proposed approach effectively handles a dynamic attribute generalization in data mining.
KW - Rough sets
KW - Knowledge discovery
KW - Data mining
KW - Incomplete information systems
UR - http://ecm.sckcen.be/OTCS/llisapi.dll/open/ezp_80899
UR - http://knowledgecentre.sckcen.be/so2/bibref/4268
U2 - 10.1016/j.knosys.2007.01.002
DO - 10.1016/j.knosys.2007.01.002
M3 - Article
SN - 0950-7051
VL - 20
SP - 485
EP - 494
JO - Knowledge-Based Systems
JF - Knowledge-Based Systems
IS - 5
ER -