Spatial tessellations: concepts and applications of Voronoi diagrams
Spatial tessellations: concepts and applications of Voronoi diagrams
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
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Efficient simplification of point-sampled surfaces
Proceedings of the conference on Visualization '02
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Approximating Bounded, Non-Orientable Surfaces from Points
SMI '04 Proceedings of the Shape Modeling International 2004
IEEE Computer Graphics and Applications
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
Self-splitting competitive learning: a new on-line clustering paradigm
IEEE Transactions on Neural Networks
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Bounds on the k-neighborhood for locally uniformly sampled surfaces
SPBG'04 Proceedings of the First Eurographics conference on Point-Based Graphics
A new method for non-spherical and multi-density clustering
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Improving DBSCAN's execution time by using a pruning technique on bit vectors
Pattern Recognition Letters
The algorithm APT to classify in concurrence of latency and drift
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Multi-scale decomposition of point process data
Geoinformatica
Hi-index | 0.10 |
In this paper, a new spatial clustering algorithm TRICLUST based on Delaunay triangulation is proposed. This algorithm treats clustering task by analyzing statistical features of data. For each data point, its values of statistical features are extracted from its neighborhood which effectively models the data proximity. By applying specifically built criteria function, TRICLUST is able to effectively handle data set with clusters of complex shapes and non-uniform densities, and with large amount of noises. One additional advantage of TRICLUST is the boundary detection function which is valuable for many real world applications such as geo-spatial data processing, point-based computer graphics, etc.