Abstract
An intersection-and-combination strategy for clustering spatial point data in the presence of obstacles (e.g. mountain) and facilitators (e.g. highway) is proposed in this paper, and an adaptive spatial clustering algorithm, called ASCDT+, is also developed. The ASCDT+ algorithm can take both obstacles and facilitators into account without additional preprocessing, and automatically detects spatial clusters adjacent to each other with arbitrary shapes and/or different densities. In addition, the ASCDT+ algorithm has the ability to find clustering patterns at both global and local levels so that users can make a more complete interpretation of the clustering results. Several simulated and real-world datasets are utilized to evaluate the effectiveness of the ASCDT+ algorithm. Comparison with two related algorithms, AUTOCLUST+ and DBRS+, demonstrates the advantages of the ASCDT+ algorithm.
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