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Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-8249

Assent, I; Krieger, R; Glavic, B; Seidl, T (2008). Clustering multidimensional sequences in spatial and temporal databases. Knowledge and Information Systems (KAIS), 16(1):29-51.

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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 andmultidimensional sequences poses newchallenges 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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7 citations in Web of Science®
9 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
DDC:000 Computer science, knowledge & systems
Language:English
Date:July 2008
Deposited On:19 Dec 2008 09:54
Last Modified:27 Nov 2013 22:30
Publisher:Springer
ISSN:0219-3116
Publisher DOI:10.1007/s10115-007-0121-3

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