Attributes Reduction and Structured Selection in Automatic Cartographical Generalization Based on Rough Set

This paper presents a method for attributes reduction and structured selection of settlements in automatic cartographical generalization based on rough set. Rough set with strong ability to knowledge mining is applied to quantitatively analyze the importance of settlements attributes and attributes reduction. A case study is used to illustrate the method. The major conclusion of this study is that in the precondition without transcendental knowledge, attributes of settlements can be filtrated based on rough set so that conditional attribute sets can be optimized, and significance value of each attribute can be extracted.