Algorithms for clustering data
Algorithms for clustering data
A comparison of sequential Delaunay triangulation algorithms
Proceedings of the eleventh annual symposium on Computational geometry
A spatial data mining method by Delaunay triangulation
GIS '97 Proceedings of the 5th ACM international workshop on Advances in geographic information systems
Data compression: the complete reference
Data compression: the complete reference
The C++ standard library: a tutorial and reference
The C++ standard library: a tutorial and reference
ACM Computing Surveys (CSUR)
Algorithm 479: A minimal spanning tree clustering method
Communications of the ACM
External memory algorithms and data structures: dealing with massive data
ACM Computing Surveys (CSUR)
Computational Geometry in C
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
ACODF: a novel data clustering approach for data mining in large databases
Journal of Systems and Software - Special issue: Performance modeling and analysis of computer systems and networks
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Characteristics of streaming media stored on the Web
ACM Transactions on Internet Technology (TOIT)
A clustering algorithm based on maximal θ-distant subtrees
Pattern Recognition
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
An efficient sweep-line Delaunay triangulation algorithm
Computer-Aided Design
Dot Pattern Processing Using Voronoi Neighborhoods
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Optimal adaptive k-means algorithm with dynamic adjustment of learning rate
IEEE Transactions on Neural Networks
A location based text mining method using ANN for geospatial KDD process
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Incremental spatial clustering in data mining using genetic algorithm and R-tree
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
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This paper presents an agglomerative hierarchical clustering algorithm for spatial data. It discovers clusters of arbitrary shapes which may be nested. The algorithm uses a sweeping approach consisting of three phases: sorting is done during the preprocessing phase, determination of clusters is performed during the sweeping phase, and clusters are adjusted during the post processing phase. The properties of the algorithm are demonstrated by examples. The algorithm is also adapted to the streaming algorithm for clustering large spatial datasets.