Issue No. 01 - January (1993 vol. 15)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.184777
<p>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.</p>
threshold validity; image recognition; mutual neighborhood clustering; random data; Poisson process; image recognition; random processes
S. Smith, "Threshold Validity for Mutual Neighborhood Clustering," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 15, no. , pp. 89-92, 1993.