期刊
INFORMATION SCIENCES
卷 181, 期 18, 页码 3878-3897出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2011.04.047
关键词
Attribute reduction; Granular computing; Granules; Information tables; Labelled blocks; Rough sets
资金
- Hong Kong Research Grants Council [CUHK 446907]
Granular computing and acquisition of if-then rules are two basic issues in knowledge representation and data mining. A formal approach to granular computing with multi-scale data measured at different levels of granulations is proposed in this paper. The concept of labelled blocks determined by a surjective function is first introduced. Lower and upper label-block approximations of sets are then defined. Multi-scale granular labelled partitions and multi-scale decision granular labelled partitions as well as their derived rough set approximations are further formulated to analyze hierarchically structured data. Finally, the concept of multi-scale information tables in the context of rough set is proposed and the unravelling of decision rules at different scales in multi-scale decision tables is discussed. (C) 2011 Elsevier Inc. All rights reserved.
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