There are many methods that can be used to compute the geographic similarity between regions represented through areas in a two dimensional space. Rough set theory in mathematics is a method to approach imperfect knowledge. It is defined to be a formal approximation of a crisp set through a pair of sets called as the lower and upper approximation of the set. It has become an area of massive research for computer scientists for its applications in fields like artificial intelligence, data mining and machine learning. Apart from these, it is also applied in economics, informatics and image processing. A generalization to the rough set can be obtained by using a rough membership function. It can be used to calculate the degree to which an element belongs to a set.
Papers:
Pawlak, Z., Rough sets, international journal of computer and information sciences, vol. 11, (1982), pp. 341-356.
Liao, Weihua, The rough method for spatial data subzone similarity measurement, Journal of Geographic Information System Vol. 4 No. 1 (2012), pp. 37-45.
Li, D. Y. and Liu, C. Y., Artificial intelligence with uncertainty, Journal of software, vol. 15, no. 11, (2004), pp.1583-1594.
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