The power of sampling in knowledge discovery
PODS '94 Proceedings of the thirteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Introduction to data compression (2nd ed.)
Introduction to data compression (2nd ed.)
Information visualization in data mining and knowledge discovery
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
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 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
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Spatio-temporal datasets are often very large and difficult to analyse. Recently a lot of interest has arisen towards data-mining techniques to reduce very large spatio-temporal datasets into relevant subsets as well as to help visualisation tools to effectively display the results. Cluster-based mining methods have proven to be successful at reducing the large size of raw data by extracting 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 the data without losing important information. In this paper, we present a new hybrid approach for reducing large spatio-temporal datasets. This approach is based on the combination of density-based and graph-based clustering. Drawing on the Shared Nearest Neighbour concept, it applies the Euclidean metric distance to determine the nearest neighbour similarity. We also present and discuss the evaluation of the results for this approach.