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.
Citation:
Ira Assent, Ralph Krieger, Ralph Krieger, Boris Glavic, Thomas Seidl, "Spatial Multidimensional Sequence Clustering," icdmw, pp.343-348, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06), 2006