Spatial Multidimensional Sequence Clustering

Measurements at different time points and positions in large temporal or spatial databases requires effective and efficient data mining techniques. For several parallel measurements, finding clusters of arbitrary length and number of attributes, poses additional challenges. We present a novel algorithm capable of finding parallel clusters in different structural quality parameter values for river sequences used by hydrologists to develop measures for river quality improvements.

Authors: Assent I., Krieger R., Glavic B., Seidl T.
Published in: Proc. 1st International Workshop on Spatial and Spatio-temporal Data Mining (SSTDM 2006)In conjunction with ICDM 2006, Hong Kong
Publisher: IEEE Computer Society - Washington,USA
Sprache: EN
Jahr: 2006
Seiten: 343-348
ISBN: 0-7695-2702-7
Konferenz: SSTDM @ICDM
Typ: Tagungsbeiträge
Forschungsgebiet: Data Analysis and Knowledge Extraction