Computational geometry: an introduction
Computational geometry: an introduction
Algorithms for clustering data
Algorithms for clustering data
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
A spatial data mining method by Delaunay triangulation
GIS '97 Proceedings of the 5th ACM international workshop on Advances in geographic information systems
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Static and dynamic information organization with star clusters
Proceedings of the seventh international conference on Information and knowledge management
Sibling clustering of tree-based spatial indexes for efficient spatial query processing
Proceedings of the seventh international conference on Information and knowledge management
Clustering Categorical Data: An Approach Based on Dynamical Systems
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Triangle: Engineering a 2D Quality Mesh Generator and Delaunay Triangulator
FCRC '96/WACG '96 Selected papers from the Workshop on Applied Computational Geormetry, Towards Geometric Engineering
Fast hierarchical clustering and its validation
Data & Knowledge Engineering
Physical Database Design: the database professional's guide to exploiting indexes, views, storage, and more
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Recent works in spatial data clustering view the input data set in terms of inter-point edge lengths rather than the points themselves. Cluster detection in such a system is a matter of finding connected paths of edges whose weight is no greater than some user input threshold or cutoff value. The SMTIN algorithm[9] is one such system that uses Delaunay triangulation to compute the set of nearest neighbor edges quickly and efficiently. Experiments demonstrate a substantial performance and accuracy improvement using SMTIN in comparison to other clustering systems.The resolution of the clusters discovered in the SMTIN system is directly related to the choice of a cutoff threshold, which makes SMTIN perform poorly for input sets with clusters at multiple resolutions. In this work we introduce an edge-centric clustering method that detects clusters at multiple resolutions. Our algorithm detects differences in density among groups of points and uses multiple cutoff points in order to account for clusters at different resolutions. One of the main benefits of the multi-resolution approach of our system is the ability to accurately cluster points that other systems would consider to be noise. Experiments indicate a substantial improvement in the clustering quality of our system in comparison to SMTIN as well as the removal of the requirement of an input distance-threshold, achieved with comparable theoretical as well as actual runtime performance. We present promising directions for this new algorithm.