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Extremes in the Complexity of Computing Metric Distances Between Partitions
January 1984 (vol. 6 no. 1)
pp. 69-73
| ASCII Text | x | ||
| William H. E. Day, Robert S. Wells, "Extremes in the Complexity of Computing Metric Distances Between Partitions," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6, no. 1, pp. 69-73, January, 1984. | |||
| BibTex | x | ||
| @article{ 10.1109/TPAMI.1984.4767476, author = {William H. E. Day and Robert S. Wells}, title = {Extremes in the Complexity of Computing Metric Distances Between Partitions}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {6}, number = {1}, issn = {0162-8828}, year = {1984}, pages = {69-73}, doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.1984.4767476}, 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 - Extremes in the Complexity of Computing Metric Distances Between Partitions IS - 1 SN - 0162-8828 SP69 EP73 EPD - 69-73 A1 - William H. E. Day, A1 - Robert S. Wells, PY - 1984 VL - 6 JA - IEEE Transactions on Pattern Analysis and Machine Intelligence ER - | |||
Day [3] describes an analytical model of minimum-length sequence (MLS) metrics measuring distances between partitions of a set. By selecting suitable values of model coordinates, a user may identify within the model that metric most appropriate to his classification application. Users should understand that within the model similar metrics may nevertheless exhibit extreme differences in their computational complexities. For example, the asymptotic time complexities of two MLS metrics are known to be linear in the number of objects being partitioned; yet we establish below that the computational problem for a closely related MLS metric is NP-complete.
Citation:
William H. E. Day, Robert S. Wells, "Extremes in the Complexity of Computing Metric Distances Between Partitions," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6, no. 1, pp. 69-73, Jan. 1984, doi:10.1109/TPAMI.1984.4767476
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