Abstract
Clustering is an important concept for analysis of data in GIS. Due to the potentially large amount of data in such systems, the time complexity for clustering algorithms is critical. K-means is a popular clustering algorithm for large-scale systems because of its linear complexity. However, this requires a priori knowledge of the number of clusters and the subsequent selection of their centroids. We propose a method for K-means to find automatically the number of clusters and their associated centroids. Moreover, we consider recursive extension of the algorithm to improve visibility of the results at different levels of abstraction, in order to support the decision-making process.
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Hamfelt, A., Karlsson, M., Thierfelder, T., Valkovsky, V. (2011). Beyond K-means: Clusters Identification for GIS. In: Popovich, V., Claramunt, C., Devogele, T., Schrenk, M., Korolenko, K. (eds) Information Fusion and Geographic Information Systems. Lecture Notes in Geoinformation and Cartography(), vol 5. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19766-6_8
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DOI: https://doi.org/10.1007/978-3-642-19766-6_8
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