Map Generalization: Information Theoretic Approach to Feature Elimination Export

@misc{citeulike:2250666, abstract = {The present paper proposes an entropy based algorithm for the feature elimination in cartographic generalization. The methodology is tested on a map with 100 point symbols. The experiment demonstrates how the most conicting map symbols are detected and eliminated from the map. The optimal symbol size can be computed from the entropy model. It is shown how to modify the entropy measure in such a way that the point elimination to a high degree preserves the point clusters.}, author = {Bj\ørke, Jan T. and Myklebust, Inge}, citeulike-article-id = {2250666}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.4.7375}, keywords = {algorithm, generalisation, information-theory, selection}, posted-at = {2009-01-05 15:03:30}, priority = {0}, title = {Map Generalization: Information Theoretic Approach to Feature Elimination}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.4.7375} }

TY - GEN ID - citeulike:2250666 L3 - citeulike-article-id:2250666 N2 - The present paper proposes an entropy based algorithm for the feature elimination in cartographic generalization. The methodology is tested on a map with 100 point symbols. The experiment demonstrates how the most conicting map symbols are detected and eliminated from the map. The optimal symbol size can be computed from the entropy model. It is shown how to modify the entropy measure in such a way that the point elimination to a high degree preserves the point clusters. TI - Map Generalization: Information Theoretic Approach to Feature Elimination KW - algorithm KW - generalisation KW - information-theory KW - selection AU - Bjørke, Jan AU - Myklebust, Inge UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.4.7375 ER -