Knowledge discovery from area-class resource maps: Capturing prototype effects
This paper presents a knowledge discovery approach to extracting knowledge from area-class resource maps. Prototype theory forms the basis of the approach which consists of two major components: (1) a scheme for organizing knowledge used in categorizing geographic entities which allows for the modeling of indeterminate boundaries and non-uniform memberships within categories; and (2) a data mining method using the Expectation Maximization (EM) algorithm for extracting such knowledge from area-class maps. A case study on knowledge discovery from a soil map demonstrates the details of the approach. The study shows that knowledge for classifying geographic entities with indeterminate boundaries is embedded in area-class maps and can be extracted through data mining; and that continuous spatial variation of geographic entities can be better modeled if the knowledge discovery process retains knowledge of within-class variations as well as transitions between classes.
Cartography and Geographic Information Science
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Qi, Feng; Zhu, A. Xing; Pei, Tao; Qin, Chengzhi; and Burt, James E., "Knowledge discovery from area-class resource maps: Capturing prototype effects" (2008). Kean Publications. 2459.