Research and Application of Multiple Distance Spatial Clustering Algorithm Based on Neighborhood Searching
This paper proposes a spatial clustering algorithm based on dual distance, via utilizing the concept of feature reachable window. The clustered features are both nearest in spatial domain and very similar in attribute domain, and time complexity of the algorithm is low. Finally, this
paper extends this algorithm to multiple distance spatial clustering; each attribute's value is managed separately and doesn't interfere with each other. While calculating, this algorithm doesn't need attribute's weight, and improves the precision of the clustering results effectively.
Keywords: ATTRIBUTE DISTANCE; REACHABLE WINDOW; SPATIAL CLUSTERING; SPATIAL DATA MINING
Document Type: Research Article
Publication date: 01 October 2012
- Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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