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Threshold Validity for Mutual Neighborhood Clustering
January 1993 (vol. 15 no. 1)
pp. 89-92

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.

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Index Terms:
threshold validity; image recognition; mutual neighborhood clustering; random data; Poisson process; image recognition; random processes
Citation:
S.P. Smith, "Threshold Validity for Mutual Neighborhood Clustering," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 1, pp. 89-92, Jan. 1993, doi:10.1109/34.184777
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