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| ASCII Text | x | ||
| S.P. Smith, "Threshold Validity for Mutual Neighborhood Clustering," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 1, pp. 89-92, January, 1993. | |||
| BibTex | x | ||
| @article{ 10.1109/34.184777, author = {S.P. Smith}, title = {Threshold Validity for Mutual Neighborhood Clustering}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {15}, number = {1}, issn = {0162-8828}, year = {1993}, pages = {89-92}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.184777}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Pattern Analysis and Machine Intelligence TI - Threshold Validity for Mutual Neighborhood Clustering IS - 1 SN - 0162-8828 SP89 EP92 EPD - 89-92 A1 - S.P. Smith, PY - 1993 KW - threshold validity; image recognition; mutual neighborhood clustering; random data; Poisson process; image recognition; random processes VL - 15 JA - IEEE Transactions on Pattern Analysis and Machine Intelligence ER - | |||
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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