CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
Spatial Clustering in the Presence of Obstacles
Proceedings of the 17th International Conference on Data Engineering
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Clustering Spatial Data in the Presence of Obstacles: a Density-Based Approach
IDEAS '02 Proceedings of the 2002 International Symposium on Database Engineering & Applications
Density-based spatial clustering in the presence of obstacles and facilitators
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
DBRS: a density-based spatial clustering method with random sampling
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Survey of clustering algorithms
IEEE Transactions on Neural Networks
A density-based spatial clustering for physical constraints
Journal of Intelligent Information Systems
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In many geography-related problems, clustering technologies are widely required to identify significant areas containing spatial objects, particularly, the object with non-spatial attributes. At most of times, the resultant geographic areas should satisfy the geographic non-overlapping constraint. That is, the areas should not be overlapped with other areas. If without non-spatial attributes, most spatial clustering approaches can obtain such results. But in the presence of non-spatial attributes, many clustering methods can not guarantee this condition, since the clustering results may be dominated in non-spatial attribute domain which can not reflect the geographic constraint. In this paper, a new spatial distance measure called penalized spatial distance (PSD) is presented, and it is proofed to satisfy the condition which can guarantee the constraint. PSD achieves this by well adjusting the spatial distance between two points according to the non-spatial attribute values between them. The clustering effectiveness of PSD incorporated with CLARANS is evaluated on both artificial data sets and a real banking analysis case. It demonstrates that PSD can effectively discover the non-spatial knowledge and contribute more reasonably to spatial clustering problem solving.