Algorithms for clustering data
Algorithms for clustering data
Multiscale representation and matching of curves using codons
CVGIP: Graphical Models and Image Processing
GeoMiner: a system prototype for spatial data mining
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Clustering Algorithms
Machine Learning
Fuzzy Spatial OQL for Fuzzy Knowledge Discovery in Databases
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Spatial Data Mining: A Database Approach
SSD '97 Proceedings of the 5th International Symposium on Advances in Spatial Databases
Data Mining: the search for knowledge in databases.
Data Mining: the search for knowledge in databases.
Simultaneous Graphic Generalization of Vector Data Sets
Geoinformatica
Controlled Line Smoothing by Snakes
Geoinformatica
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The nature of map generalization may be non-uniform along the length of an individual line, requiring the application of methods that adapt to the local geometry and the geographical context. Geographical databases need to be enriched in terms of shape description structures (geometrical knowledge), knowledge of appropriate order of operations and of appropriate algorithms (procedural knowledge). Stored knowledge should take account of semantic and morphological characteristics, and of cartographic constraints.This paper proposes and discusses three experiments on knowledge acquisition using unsupervised and supervised learning techniques. In order to exploit geometrical shape knowledge, classifications were computed according to a set of morphological measures using unsupervised learning. Choice of appropriate operations was determined by the results of a test with IGN cartographers considering line characteristics. These results were given to a supervised learning algorithm, along with corresponding computed measures in order to discover rules. The approach and the resulting rules are presented and discussed. Tests have also been conducted on the tuning of parameter values, applying a Gaussian smoothing tolerance value to a set of lines using the supervised learning algorithm. The values obtained by means of the learning algorithm have been compared with interactive choices of an expert. Results are promising with a prediction rate higher than 80% having been obtained.