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
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 metrological, 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 from very large size 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 data reduction technique based on clustering to help analyse very large spatio-temporal data. We also present and discuss preliminary results of this approach.