We are working on spatial data mining for very large datasets. Spatial data mining is the discovery of interesting characteristics and patterns that may exist in large spatial databases. Usually the spatial relationships are implicit in nature. Because of the huge amounts of spatial data that may be obtained from satellite images, medical equipments, Geographic Information Systems (GIS), image database exploration etc., it is expensive and unrealistic for the users to examine spatial data in detail. Spatial data mining aims to automate the process of understanding spatial data by representing the data in a concise manner and reorganizing spatial databases to accommodate data semantics.
At this moment we are looking into clustering methods for multidimensional spatial data mining. The aim of data clustering methods is to group the objects in spatial databases into meaningful subclasses. Due to the huge amount of spatial data, an important challenge for clustering algorithms is to achieve good time efficiency. Also clusters may have arbitray shapes or relative positions. The presence of outliers make the problem even more challenging. Outliers refer to spatial objects which are not contained in any cluster and should be discarded during the mining process.
Very recently we have attacked this problem from the angle of signal processing techniques and obtained very encouraging results.
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