Multivariate statistical methods: a primer
Multivariate statistical methods: a primer
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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River valley segments are adjacent sections of streams and rivers that are relatively homogeneous in hydrology, limnology, channel morphology, riparian dynamics, and biological communities. River valley segments have been advocated as appropriate spatial units for assessing, monitoring, and managing rivers and streams for several reasons; however, methods for delineating these spatial units have been tedious to implement or have lacked objectivity, which arguably has limited their use as river and stream management units by natural resource agencies. We describe a spatially constrained clustering program that we developed for delineating river valley segments from geographic information system digital river network databases that is flexible, easy-to-use, and improves objectivity in the river valley segment delineation process. This program, which we refer to as the valley segment affinity search technique (VAST), includes a variety of options for determining spatial adjacency in stream reaches, as well as several data transformation methods, types of resemblance coefficients, and cluster linkage methods. The usefulness of VAST is demonstrated by using it to delineate river valley segments for river network databases for Michigan and Wisconsin, USA, and by comparing river valley segments delineated by VAST to an expert-opinion delineation previously completed for a Michigan river network database.