T. Y. Lin, “Data Mining and Machine Oriented Modeling: A Granular Computing Approach,” Applied Intelligence, Vol. 13, No. 2, 2000, pp. 113-124. doi:10.1023/A:1008384328214

has been cited by the following article:

• TITLE: The Rough Method for Spatial Data Subzone Similarity Measurement

  AUTHORS: Weihua Liao

  KEYWORDS: Subzone; Rough Sets; Neighborhood Rough Sets; Similarity Measurement

  JOURNAL NAME: Journal of Geographic Information System, Vol.4 No.1, January 17, 2012

  ABSTRACT: There are two methods for GIS similarity measurement problem, one is cross-coefficient for GIS attribute similarity measurement, and the other is spatial autocorrelation that is based on spatial location. These methods can not calculate subzone similarity problem based on universal background. The rough measurement based on membership function solved this problem well. In this paper, we used rough sets to measure the similarity of GIS subzone discrete data, and used neighborhood rough sets to calculate continuous data’s upper and lower approximation. We used neighborhood particle to calculate membership function of continuous attribute, then to solve continuous attribute’s subzone similarity measurement problem.