The planar k-means problem is NP-hard
暂无分享,去创建一个
[1] Nimrod Megiddo,et al. On the Complexity of Some Common Geometric Location Problems , 1984, SIAM J. Comput..
[2] Alan M. Frieze,et al. Clustering Large Graphs via the Singular Value Decomposition , 2004, Machine Learning.
[3] Sergei Vassilvitskii,et al. Worst-case and Smoothed Analysis of the ICP Algorithm, with an Application to the k-means Method , 2006, 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06).
[4] László Lovász,et al. Algorithmic theory of numbers, graphs and convexity , 1986, CBMS-NSF regional conference series in applied mathematics.
[5] David Lichtenstein,et al. Planar Formulae and Their Uses , 1982, SIAM J. Comput..
[6] S. P. Lloyd,et al. Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.
[7] Leslie G. Valiant,et al. Universality considerations in VLSI circuits , 1981, IEEE Transactions on Computers.
[8] Eric Allender,et al. The Directed Planar Reachability Problem , 2005, FSTTCS.
[9] Sergei Vassilvitskii,et al. How slow is the k-means method? , 2006, SCG '06.
[10] Charles E. Leiserson,et al. Area-efficient graph layouts , 1980, 21st Annual Symposium on Foundations of Computer Science (sfcs 1980).
[11] Matt Gibson,et al. On clustering to minimize the sum of radii , 2008, SODA '08.
[12] Charles E. Leiserson,et al. Area-Efficient Graph Layouts (for VLSI) , 1980, FOCS.
[13] Sariel Har-Peled,et al. How Fast Is the k-Means Method? , 2005, SODA '05.
[14] Marek Karpinski,et al. Approximation schemes for clustering problems , 2003, STOC '03.
[15] Steven A. Orszag,et al. CBMS-NSF REGIONAL CONFERENCE SERIES IN APPLIED MATHEMATICS , 1978 .
[16] Sanjeev Arora,et al. Polynomial time approximation schemes for Euclidean TSP and other geometric problems , 1996, Proceedings of 37th Conference on Foundations of Computer Science.
[17] Rafail Ostrovsky,et al. The Effectiveness of Lloyd-Type Methods for the k-Means Problem , 2006, FOCS.
[18] Mary Inaba,et al. Applications of weighted Voronoi diagrams and randomization to variance-based k-clustering: (extended abstract) , 1994, SCG '94.
[19] S. Dasgupta. The hardness of k-means clustering , 2008 .
[20] Amit Kumar,et al. A simple linear time (1 + /spl epsiv/)-approximation algorithm for k-means clustering in any dimensions , 2004, 45th Annual IEEE Symposium on Foundations of Computer Science.
[21] David M. Mount,et al. A local search approximation algorithm for k-means clustering , 2002, SCG '02.
[22] Sergei Vassilvitskii,et al. k-means++: the advantages of careful seeding , 2007, SODA '07.
[23] Pierre Hansen,et al. NP-hardness of Euclidean sum-of-squares clustering , 2008, Machine Learning.