Pattern extraction by using both spatial and temporal features on Turkish meteorological data

Thesis Type: Postgraduate

Institution Of The Thesis: Orta Doğu Teknik Üniversitesi, Faculty of Engineering, Department of Computer Engineering, Turkey

Approval Date: 2010




With the growth in the size of datasets, data mining has been an important research topic and is receiving substantial interest from both academia and industry for many years. Especially, spatio-temporal data mining, mining knowledge from large amounts of spatio-temporal data, is a highly demanding field because huge amounts of spatio-temporal data are collected in various applications. Therefore, spatio-temporal data mining requires the development of novel data mining algorithms and computational techniques for a successful analysis of large spatio-temporal databases. In this thesis, a spatio-temporal mining technique is proposed and applied on Turkish meteorological data which has been collected from various weather stations in Turkey. This study also includes an analysis and interpretation of spatio-temporal rules generated for Turkish Meteorological data set. We introduce a second level mining technique which is used to define general trends of the patterns according to the spatial changes. Genarated patterns are investigated under different temporal sets in order to monitor the changes of the events with respect to temporal changes.