## IIT Database Group

### 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.

### bibtex

@inproceedings{AK06,
author = {Assent, Ira and Krieger, Ralph and Glavic, Boris and Seidl, Thomas},
bibsource = {DBLP, http://dblp.uni-trier.de},
booktitle = {Proceedings of the 1st International Workshop on Spatial and Spatio-temporal Data Mining collocated with ICDM},
date-added = {2012-12-14 18:55:49 +0000},
date-modified = {2012-12-14 18:55:49 +0000},
ee = {http://doi.ieeecomputersociety.org/10.1109/ICDMW.2006.153},
keywords = {Data Mining},
local-url = {file://localhost/Users/admin/Documents/Uni/IFI/Papers/AKGS06_Spatial%20Multidimensional%20Sequence%20Clustering_0.pdf},
pages = {343-348},
pdfurl = {http://cs.iit.edu/%7edbgroup/assets/pdfpubls/AK06.pdf},
title = {{Spatial Multidimensional Sequence Clustering}},
venueshort = {SSTDM},
year = {2006},
bdsk-url-1 = {http://cs.iit.edu/%7edbgroup/assets/pdfpubls/AK06.pdf}
}


### Reference

Spatial Multidimensional Sequence Clustering Ira Assent, Ralph Krieger, Boris Glavic and Thomas Seidl Proceedings of the 1st International Workshop on Spatial and Spatio-temporal Data Mining collocated with ICDM (2006), pp. 343–348.