Robust regression and outlier detection
Robust regression and outlier detection
Spatial tessellations: concepts and applications of Voronoi diagrams
Spatial tessellations: concepts and applications of Voronoi diagrams
Design models and functionality in GIS
Computers & Geosciences - Special issue on GIS design models
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
A spatial data mining method by clustering analysis
Proceedings of the 6th ACM international symposium on Advances in geographic information systems
LEDA: a platform for combinatorial and geometric computing
LEDA: a platform for combinatorial and geometric computing
Finding Aggregate Proximity Relationships and Commonalities in Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
The Delauney Triangulation Closely Approximates the Complete Euclidean Graph
WADS '89 Proceedings of the Workshop on Algorithms and Data Structures
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovering Associations in Spatial Data - An Efficient Medoid Based Approach
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Finding Boundary Shape Matching Relationships in Spatial Data
SSD '97 Proceedings of the 5th International Symposium on Advances in Spatial Databases
Proceedings of the International Conference GIS - From Space to Territory: Theories and Methods of Spatio-Temporal Reasoning on Theories and Methods of Spatio-Temporal Reasoning in Geographic Space
The BANG-Clustering System: Grid-Based Data Analysis
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
STING+: An Approach to Active Spatial Data Mining
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Low Degree Algorithms for Computing and Checking Gabriel Graphs
Low Degree Algorithms for Computing and Checking Gabriel Graphs
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
Hybrid genetic algorithms are better for spatial clustering
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
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
An IACO and HPSO Method for Spatial Clustering with Obstacles Constraints
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Spatial Clustering with Obstacles Constraints by Hybrid Particle Swarm Optimization with GA Mutation
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
A Particle Swarm Optimization Method for Spatial Clustering with Obstacles Constraints
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Obstacle clustering and outlier detection
Proceedings of the 48th Annual Southeast Regional Conference
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
GIS enabled service site selection: Environmental analysis and beyond
Information Systems Frontiers
A density-based spatial clustering for physical constraints
Journal of Intelligent Information Systems
Towards an ontology-based spatial clustering framework
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
A novel spatial clustering with obstacles constraints based on PNPSO and k-medoids
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Absolute and relative clustering
Proceedings of the 4th MultiClust Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering
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Wide spread clustering algorithms use the Euclidean distance to measure spatial proximity. However, obstacles in other GIS data-layers prevent traversing the straight path between two points. AUTOCLUST+ clusters points in the presence of obstacles based on Voronoi modeling and Delaunay Diagrams. The algorithm is free of usersupplied arguments and incorporates global and local variations. Thus, it detects high-quality clusters (clusters of arbitrary shapes, clusters of different densities, sparse clusters adjacent to high-density clusters, multiple bridges between clusters and closely located high-density clusters) without prior knowledge. Consequently, it successfully supports correlation analyses between layers (requiring high-quality clusters) and more general locational optimization problems in the presence of obstacles. All this within O(n log n+[m+R] log n) expected time, where n is the number of data points, m is the number of line-segments that determine the obstacles and R is the number of Delaunay edges intersecting some obstacles. A series of detailed performance evaluations illustrates the power of AUTOCLUST+ and confirms the virtues of our approach.