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Threshold validity for mutual neighborhood clustering

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1 Author(s)
S. P. Smith ; Northrop Res. & Technol. Center, Palos Verdes Peninsula, CA, USA

Clustering algorithms have the annoying characteristic of finding clusters in random data. A theoretical analysis of the threshold of the mutual neighborhood clustering algorithm (MNCA) under the hypothesis of random data is presented. This yields a theoretical minimum value of this threshold below which even unclustered data are broken into separate clusters. To derive the threshold, a theorem about mutual near neighbors in a Poisson process is stated and proved. Simple experiments demonstrate the usefulness of the theoretical thresholds

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:15 ,  Issue: 1 )