Algorithms & data structures
Algorithms for clustering data
Algorithms for clustering data
GeoMiner: a system prototype for spatial data mining
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Algorithm 781: generating Hilbert's space-filling curve by recursion
ACM Transactions on Mathematical Software (TOMS)
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Proceedings of the 1999 workshop on new paradigms in information visualization and manipulation in conjunction with the eighth ACM internation conference on Information and knowledge management
Data mining: concepts and techniques
Data mining: concepts and techniques
Analysis of the Clustering Properties of the Hilbert Space-Filling Curve
IEEE Transactions on Knowledge and Data Engineering
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Spatial Data Mining: A Database Approach
SSD '97 Proceedings of the 5th International Symposium on Advances in Spatial Databases
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Analysis of Multi-Dimensional Space-Filling Curves
Geoinformatica
Effect ordering for data displays
Computational Statistics & Data Analysis - Data visualization
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Visual Data Mining in Large Geospatial Point Sets
IEEE Computer Graphics and Applications
Data Mining: Next Generation Challenges and Future Directions
Data Mining: Next Generation Challenges and Future Directions
Global visualization and alignments of whole bacterial genomes
IEEE Transactions on Visualization and Computer Graphics
On the metric properties of discrete space-filling curves
IEEE Transactions on Image Processing
A Visualization System for Space-Time and Multivariate Patterns (VIS-STAMP)
IEEE Transactions on Visualization and Computer Graphics
Visual analytics of spatial interaction patterns for pandemic decision support
International Journal of Geographical Information Science - Geovisual Analytics for Spatial Decision Support
All-nearest-neighbors finding based on the Hilbert curve
Expert Systems with Applications: An International Journal
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Geographic information (e.g., locations, networks, and nearest neighbors) are unique and different from other aspatial attributes (e.g., population, sales, or income). It is a challenging problem in spatial data mining and visualization to take into account both the geographic information and multiple aspatial variables in the detection of patterns. To tackle this problem, we present and evaluate a variety of spatial ordering methods that can transform spatial relations into a one-dimensional ordering and encoding which preserves spatial locality as much possible. The ordering can then be used to spatially sort temporal or multivariate data series and thus help reveal patterns across different spaces. The encoding, as a materialization of spatial clusters and neighboring relations, is also amenable for processing together with aspatial variables by any existing (non-spatial) data mining methods. We design a set of measures to evaluate nine different ordering/encoding methods, including two space-filling curves, six hierarchical clustering based methods, and a one-dimensional Sammon mapping (a multidimensional scaling approach). Evaluation results with various data distributions show that the optimal ordering/encoding with the complete-linkage clustering consistently gives the best overall performance, surpassing well-known space-filling curves in preserving spatial locality. Moreover, clustering-based methods can encode not only simple geographic locations, e.g., x and y coordinates, but also a wide range of other spatial relations, e.g., network distances or arbitrarily weighted graphs.