Clustering algorithms for area geographical entities in spatial data mining

Abstract

Spatial data mining is the process of identifying or extracting efficient, novel, potentially useful and ultimately understandable patterns from the spatial data set, the spatial clustering analysis is one of the most important research directions in spatial data mining. Clustering criterion implied in massive data can be discovered by spatial clustering analysis method which can be used to explore deeper level knowledge combined with other data mining methods and to improve the efficiency and quality of data mining. We studied clustering algorithms of area geographical entities based on geometric shape similarity. And we presented a similarity criterion of line segments shape and a criterion of area geographical entities comprehensively utilizing distance and geometric shape similarity. Clustering algorithms based on these criterions are more suitable for clustering analysis of area geographical entities.