Spatial Multidimensional Sequence Clustering

Publication type:

inproceedings

Authors:

Ira Assent, Ralph Krieger, Boris Glavic, Thomas Seidl

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.

Title:

Spatial Multidimensional Sequence Clustering

Year:

2006

bibsource:

DBLP, http://dblp.uni-trier.de

booktitle:

SSTDM '06: Proc. 1st International Workshop on Spatial and Spatio-temporal Data Mining In conjunction with ICDM

crossref:

DBLP:conf/icdm/2006w

ee:

http://doi.ieeecomputersociety.org/10.1109/ICDMW.2006.153