Efficient Density Clustering Method for Large Spatial Data Using HOBBit Rings

Data mining for spatial data has become increasingly important as more and more organizations are exposed to spatial data from such sources as remote sensing, geographical information systems (GIS), astronomy, computer cartography, environmental assessment and planning, bioinformatics, etc. Recently, density based clustering methods, such as DENCLUE, DBSCAN, OPTICS, have been published and recognized as powerful clustering methods for Data Mining. These approaches have run time complexity of ) log ( n n O when using spatial index techniques, R tree and grid cell. However, these methods are known to lack scalability with respect to dimensionality. In this paper, we develop a new efficient density based clustering algorithm using HOBBit metrics and P-trees. The fast P-tree ANDing operation facilitates the calculation of the density function within HOBBit rings. The average run time complexity of our algorithm for spatial data in d-dimension is ) ( n dn O . Our proposed method has comparable cardinality scalability with other density methods for small and medium size of data, but superior dimensional scalability.