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The characteristics of a landscape and drainage size defines the abiotic character of the stream. We advance on an existing clustering method by making it faster, more stable, capable of processing large areas, and adding several new user defined options. The software clusters interconfluence stream reaches into larger homogenous stream segments using a series of rules about network adjacency, upstream catchment area, and similarity of landscape characteristics. Landscape variables and clustering parameters are discussed to provide guidance for helping users achieve their clustering objectives. We provide an example of clustering stream reaches in Ontario, Canada that was used to develop a provincial aquatic classification. [ABSTRACT FROM AUTHOR]