Knowledge discovery from area-class resource maps: Capturing prototype effects
Document Type
Article
Publication Date
10-1-2008
Abstract
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.
Publication Title
Cartography and Geographic Information Science
First Page Number
223
Last Page Number
237
DOI
10.1559/152304008786140533
Recommended Citation
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.
https://digitalcommons.kean.edu/keanpublications/2459