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Clustering multidimensional sequences in spatial and temporal databases

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Abstract

Many environmental, scientific, technical or medical database applications require effective and efficient mining of time series, sequences or trajectories of measurements taken at different time points and positions forming large temporal or spatial databases. Particularly the analysis of concurrent and multidimensional sequences poses new challenges in finding clusters of arbitrary length and varying number of attributes. We present a novel algorithm capable of finding parallel clusters in different subspaces and demonstrate our results for temporal and spatial applications. Our analysis of structural quality parameters in rivers is successfully used by hydrologists to develop measures for river quality improvements.

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Authors and Affiliations

  1. Data Management and Exploration Group, RWTH Aachen University, Aachen, Germany

    Ira Assent, Ralph Krieger, Boris Glavic & Thomas Seidl

Authors
  1. Ira Assent
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  2. Ralph Krieger
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  3. Boris Glavic
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  4. Thomas Seidl
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Correspondence to Ira Assent.

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Assent, I., Krieger, R., Glavic, B. et al. Clustering multidimensional sequences in spatial and temporal databases. Knowl Inf Syst 16, 29–51 (2008). https://doi.org/10.1007/s10115-007-0121-3

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  • DOI: https://doi.org/10.1007/s10115-007-0121-3

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