Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Concept decompositions for large sparse text data using clustering
Machine Learning
Information visualization in data mining and knowledge discovery
Information visualization in data mining and knowledge discovery
Machine Learning
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
"GeoPlot": spatial data mining on video libraries
Proceedings of the eleventh international conference on Information and knowledge management
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Inventing discovery tools: combining information visualization with data mining
Information Visualization
Visual Data Mining in Large Geospatial Point Sets
IEEE Computer Graphics and Applications
Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach
Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach
Towards a framework for mining and analysing spatio-temporal datasets
International Journal of Geographical Information Science - Geovisual Analytics for Spatial Decision Support
Advancing Spatio-temporal Analysis of Ecological Data: Examples in R
ICCSA '08 Proceeding sof the international conference on Computational Science and Its Applications, Part I
Scalable 2-Pass Data Mining Technique for Large Scale Spatio-temporal Datasets
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
HyperSmooth: A System for Interactive Spatial Analysis Via Potential Maps
W2GIS '08 Proceedings of the 8th International Symposium on Web and Wireless Geographical Information Systems
GWVis: A tool for comparative ground-water data visualization
Computers & Geosciences
A clustering-based data reduction for very large spatio-temporal datasets
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Geovisual evaluation of public participation in decision making: The grapevine
Journal of Visual Languages and Computing
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
A visual analytics system for metropolitan transportation
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
A new hybrid clustering method for reducing very large spatio-temporal dataset
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
Context Based Positive and Negative Spatio-Temporal Association Rule Mining
Knowledge-Based Systems
International Journal of Multimedia Data Engineering & Management
Interaction analysis and joint attention tracking in augmented reality
Proceedings of the 15th ACM on International conference on multimodal interaction
3D geovisualisation techniques applied in spatial data mining
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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Spatio-temporal data sets are often very large and difficult to analyze and display. Since they are fundamental for decision support in many application contexts, recently a lot of interest has arisen toward data-mining techniques to filter out relevant subsets of very large data repositories as well as visualization tools to effectively display the results. In this paper we propose a data-mining system to deal with very large spatio-temporal data sets. Within this system, new techniques have been developed to efficiently support the data-mining process, address the spatial and temporal dimensions of the data set, and visualize and interpret results. In particular, two complementary 3D visualization environments have been implemented. One exploits Google Earth to display the mining outcomes combined with a map and other geographical layers, while the other is a Java3D-based tool for providing advanced interactions with the data set in a non-geo-referenced space, such as displaying association rules and variable distributions.