On Data Clustering Analysis: Scalability, Constraints, and Validation
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A hybrid spatial data clustering method for site selection: The data driven approach of GIS mining
Expert Systems with Applications: An International Journal
Continuous visible nearest neighbor queries
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Continuous obstructed nearest neighbor queries in spatial databases
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Geodesic disks and clustering in a simple polygon
ISAAC'07 Proceedings of the 18th international conference on Algorithms and computation
Spatial clustering based on moving distance in the presence of obstacles
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Density-based semi-supervised clustering
Data Mining and Knowledge Discovery
Continuous nearest-neighbor search in the presence of obstacles
ACM Transactions on Database Systems (TODS)
Continuous visible nearest neighbor query processing in spatial databases
The VLDB Journal — The International Journal on Very Large Data Bases
On efficient obstructed reverse nearest neighbor query processing
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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Clustering spatial data is a well-known problem that has been extensively studied. Grouping similar data in large 2-dimensional spaces to find hidden patterns or meaningful sub-groups has many applications such as satellite imagery, geographic information systems, medical image analysis, marketing, computer visions, etc. Although many methods have been proposed in the literature, very few have considered physical obstacles that may have significant consequences on the effectiveness of the clustering. Taking into account these constraints during the clustering process is costly and the modeling of the constraints is paramount forgood performance. In this paper, we investigate the problem of clustering in the presence of constraints such as physical obstacles and introduce a new approach to model these constraints using polygons. We also propose a strategy to prune the search space and reduce the number of polygons to test during clustering. We devise a density-based clusteringalgorithm, DBCluC, which takes advantage of our constraint modeling to efficiently cluster data objects while considering all physical constraints. The algorithm can detect clusters of arbitrary shape and is insensitive to noise, the input order, and the difficulty of constraints. Its average running complexity is O(NlogN) where N is the number of data points.