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|
|Type:||Conference papers (peer reviewed)|
|Research topic:||Data Analysis and Knowledge Extraction|