A Survey on Data Mining Methods for Clustering Complex Spatiotemporal Data

Conference paper

DOI: 10.1007/978-3-319-58274-0_10

Part of the Communications in Computer and Information Science book series (CCIS, volume 716)
Cite this paper as:
Maciąg P.S. (2017) A Survey on Data Mining Methods for Clustering Complex Spatiotemporal Data. In: Kozielski S., Mrozek D., Kasprowski P., Małysiak-Mrozek B., Kostrzewa D. (eds) Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation. BDAS 2017. Communications in Computer and Information Science, vol 716. Springer, Cham

Abstract

This publication presents a survey on the clustering algorithms proposed for spatiotemporal data. We begin our study with definitions of spatiotemporal datatypes. Next we provide a categorization of spatiotemporal datatypes with the special emphasis on the spatial representation and diversity in temporal aspect. We conduct our deliberation focusing mainly on the complex spatiotemporal objects. In particular, we review algorithms for two problems already proposed in literature: clustering complex spatiotemporal objects as polygons or geographical areas and measuring distances between complex spatial objects. In addition to description of the problems mentioned above, we also attempt to provide a comprehensive references review and provide a general look on the different problems related to the clustering spatiotemporal data.

Keywords

Data mining Clustering spatiotemporal data Clustering algorithms 

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Computer ScienceWarsaw University of TechnologyWarsawPoland

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