Whelan, MichaelMichaelWhelan2022-05-052022-05-052020 the A2020http://hdl.handle.net/10197/12846Today, huge amounts of data are being collected with spatial and temporal components from sources such as meteorological, satellite imagery, etc. Efficient analysis of this type of data is therefore very challenging and becoming a massive economic need. The research area of spatio-temporal data mining, has emerged, where innovative compu- tational techniques are being applied to the analysis of these very large spatio-temporal databases. The size of these databases and the rate that they are being produced is a major limiting factor on performing on-time data analysis. Therefore, there is a need for efficient pre-processing techniques to prepare the data effectively before analysis. In this thesis, we present our data reduction framework for very large spatio-temporal data sets. This framework incorporates our data compression model, based on density- based clustering techniques, to reduce spatio-temporal data. We describe firstly each technique, and then we compare them in an analytical way. Furthermore, we evaluate our model on real world data sets.enData reductionClusteringVisualisationSpatio-temporal dataMaster Thesis2021-12-08https://creativecommons.org/licenses/by-nc-nd/3.0/ie/