Efficient Discovery of Sequential Patterns from Event-Based Spatio-Temporal Data by Applying Microclustering Approach
AbstractDiscovering various types of frequent patterns in spatiotemporal data is gaining attention of researchers nowadays. We consider spatiotemporal data represented in the form of events, each associated with location, type and occurrence time. The problem is to discover all signi�- cant sequential patterns denoting spatial and temporal relations between event types. In the paper, we adapted a microclustering approach and use it to effectively and effciently discover sequential patterns and to reduce size of dataset of instances. Appropriate indexing structure has been proposed and notions already de�ned in the literature have been reformulated. We modify algorithms already de�ned in literature and propose an algorithm called Micro-ST-Miner for discovering sequential patterns in event-based spatiotemporal data.
|Publication size in sheets||0.8|
|Book||Bembenik Robert, Skonieczny Łukasz, Protaziuk Grzegorz M., Kryszkiewicz Marzena, Rybiński Henryk (eds.): Intelligent Methods and Big Data in Industrial Applications, Studies in Big Data, vol. 40, 2019, Springer International Publishing, ISBN 978-3-319-77603-3, [978-3-319-77604-0], 376 p., DOI:10.1007/978-3-319-77604-0|
|project||Development of new algorithms in the areas of software and computer architecture, artificial intelligence and information systems and computer graphics . Project leader: Arabas Jarosław,
, Phone: +48 22 234 7432, start date 01-06-2017, end date 31-10-2018, II/2017/DS/1, Completed
|Score|| = 15.0, BookChapterSeriesAndMatConf|
= 15.0, BookChapterSeriesAndMatConf
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