The power of sampling in knowledge discovery
PODS '94 Proceedings of the thirteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
The object data standard: ODMG 3.0
The object data standard: ODMG 3.0
Introduction to data compression (2nd ed.)
Introduction to data compression (2nd ed.)
Information visualization in data mining and knowledge discovery
Time Granularities in Databases, Data Mining and Temporal Reasoning
Time Granularities in Databases, Data Mining and Temporal Reasoning
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
On Issues of Instance Selection
Data Mining and Knowledge Discovery
An Updated Bibliography of Temporal, Spatial, and Spatio-temporal Data Mining Research
TSDM '00 Proceedings of the First International Workshop on Temporal, Spatial, and Spatio-Temporal Data Mining-Revised Papers
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Exploratory spatio-temporal data mining and visualization
Journal of Visual Languages and Computing
Clustering Using a Similarity Measure Based on Shared Near Neighbors
IEEE Transactions on Computers
Towards a framework for mining and analysing spatio-temporal datasets
International Journal of Geographical Information Science - Geovisual Analytics for Spatial Decision Support
Data Reduction in Very Large Spatio-Temporal Datasets
WETICE '10 Proceedings of the 2010 19th IEEE International Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises
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
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Today, huge amounts of data are being collected with spatial and temporal components from sources such as meteorological, satellite imagery etc. Efficient visualisation as well as discovery of useful knowledge from these datasets is therefore very challenging and becoming a massive economic need. Data Mining has emerged as the technology to discover hidden knowledge in very large amounts of data. Furthermore, data mining techniques could be applied to decrease the large size of raw data by retrieving its useful knowledge as representatives. As a consequence, instead of dealing with a large size of raw data, we can use these representatives to visualise or to analyse without losing important information. This paper presents a new approach based on different clustering techniques for data reduction to help analyse very large spatio-temporal data. We also present and discuss preliminary results of this approach.