BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
A Unified Approach to Detecting Spatial Outliers
Geoinformatica
IEEE Transactions on Knowledge and Data Engineering
Dual Clustering: Integrating Data Clustering over Optimization and Constraint Domains
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Reconstructing domain boundaries within a given set of points, using Delaunay triangulation
Computers & Geosciences
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
The self-organizing map, the Geo-SOM, and relevant variants for geosciences
Computers & Geosciences
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
Category role aided market segmentation approach to convenience store chain category management
Decision Support Systems
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Geometrical properties and attributes are two important characteristics of a spatial object. In previous spatial clustering studies, these two characteristics were often neglected. This paper addresses the problem of how to accommodate geometrical properties and attributes in spatial clustering. A new density-based spatial clustering algorithm (DBSC) is developed by considering both spatial proximity and attribute similarity. Delaunay triangulation with edge length constraints is first employed for modeling the spatial proximity relationships among spatial objects. A modified density-based clustering strategy is then designed and used to identify spatial clusters. Objects in the same cluster detected by the DBSC algorithm are proximal in a spatial domain and similar in an attribute domain. In addition, the algorithm is able to detect clusters of arbitrary shapes and non-homogeneous densities in the presence of noise. The effectiveness and practicability of the DBSC algorithm are validated using both simulated and real spatial datasets.