Spatial Multidimensional Sequence Clustering

Authors:
Ira Assent;Ralph Krieger;Boris Glavic;Thomas Seidl
Affiliations:
RWTH Aachen University, Germany;RWTH Aachen University, Germany;RWTH Aachen University, Germany;RWTH Aachen University, Germany
Venue:
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Year:
2006

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Abstract

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.