DocumentCode
3260046
Title
Spatial Multidimensional Sequence Clustering
Author
Assent, Ira ; Krieger, Ralph ; Glavic, Boris ; Seidl, Thomas
Author_Institution
Data Manage. & Exploration Group, RWTH Aachen Univ.
fYear
2006
fDate
Dec. 2006
Firstpage
343
Lastpage
348
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
Keywords
environmental science computing; pattern clustering; data mining techniques; parallel clusters; river quality improvements; river sequences; spatial databases; spatial multidimensional sequence clustering; structural quality parameter values; temporal databases; Clustering algorithms; Data mining; Hydrologic measurements; Hydrology; Length measurement; Multidimensional systems; Position measurement; Rivers; Spatial databases; Time measurement;
fLanguage
English
Publisher
ieee
Conference_Titel
Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on
Conference_Location
Hong Kong
Print_ISBN
0-7695-2702-7
Type
conf
DOI
10.1109/ICDMW.2006.153
Filename
4063651
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