Algorithms for clustering data
Algorithms for clustering data
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
A spatial data mining method by Delaunay triangulation
GIS '97 Proceedings of the 5th ACM international workshop on Advances in geographic information systems
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
Primitives for the manipulation of general subdivisions and the computation of Voronoi
ACM Transactions on Graphics (TOG)
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Algorithms for Model-Based Gaussian Hierarchical Clustering
SIAM Journal on Scientific Computing
Entropy-based subspace clustering for mining numerical data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Computing Surveys (CSUR)
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Towards an effective cooperation of the user and the computer for classification
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering spatial data using random walks
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A Monte Carlo algorithm for fast projective clustering
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Information Retrieval
Spatial Clustering in the Presence of Obstacles
Proceedings of the 17th International Conference on Data Engineering
Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering
VLDB '99 Proceedings of the 25th 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
Model-Based Hierarchical Clustering
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Grid-Clustering: An Efficient Hierarchical Clustering Method for Very Large Data Sets
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Information Visualization - Special issue on coordinated and multiple views in exploratory visualization
A Visualization System for Space-Time and Multivariate Patterns (VIS-STAMP)
IEEE Transactions on Visualization and Computer Graphics
Spatial ordering and encoding for geographic data mining and visualization
Journal of Intelligent Information Systems
Visual analytics of spatial interaction patterns for pandemic decision support
International Journal of Geographical Information Science - Geovisual Analytics for Spatial Decision Support
Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP)
International Journal of Geographical Information Science
A discrete particle swarm optimization algorithm with fully communicated information
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Hierarchical cluster visualization in web mapping systems
Proceedings of the 19th international conference on World wide web
Exploratory hierarchical clustering for management zone delineation in precision agriculture
ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
Fast GPU-based locality sensitive hashing for k-nearest neighbor computation
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
A new and efficient k-medoid algorithm for spatial clustering
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part III
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The unprecedented large size and high dimensionality of existing geographic datasets make the complex patterns that potentially lurk in the data hard to find. Clustering is one of the most important techniques for geographic knowledge discovery. However, existing clustering methods have two severe drawbacks for this purpose. First, spatial clustering methods focus on the specific characteristics of distributions in 2- or 3-D space, while general-purpose high-dimensional clustering methods have limited power in recognizing spatial patterns that involve neighbors. Second, clustering methods in general are not geared toward allowing the human-computer interaction needed to effectively tease-out complex patterns. In the current paper, an approach is proposed to open up the “black box” of the clustering process for easy understanding, steering, focusing and interpretation, and thus to support an effective exploration of large and high dimensional geographic data. The proposed approach involves building a hierarchical spatial cluster structure within the high-dimensional feature space, and using this combined space for discovering multi-dimensional (combined spatial and non-spatial) patterns with efficient computational clustering methods and highly interactive visualization techniques. More specifically, this includes the integration of: (1) a hierarchical spatial clustering method to generate a 1-D spatial cluster ordering that preserves the hierarchical cluster structure, and (2) a density- and grid-based technique to effectively support the interactive identification of interesting subspaces and subsequent searching for clusters in each subspace. The implementation of the proposed approach is in a fully open and interactive manner supported by various visualization techniques.