Efficient Density Clustering Method for Spatial Data

Abstract

Data mining for spatial data has become increasingly important as more and more organizations are exposed to spatial data from sources such as remote sensing, geographical information systems, astronomy, computer cartography, environmental assessment and planning, 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 O(nlogn) 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, a unique approach to efficient neighborhood search and a new efficient density based clustering algorithm using EIN-rings are developed. Our approach exploits compressed vertical data structures, Peano Trees (P-trees), and fast P-tree logical operations to accelerate the calculation of the density function within EIN-rings. This approach stands in contrast to the ubiquitous approach of vertically scanning horizontal data structures (records). The average run time complexity of our algorithm for spatial data in d-dimension is $O(dn\sqrt{n})$. Our proposed method has comparable cardinality scalability with other density methods for small and medium size of data, but superior speed and dimensional scalability.

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.