T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu. An efficient k-means clustering algorithm: Analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell., 24:881--892, 2002. Home/Search Document Not in Database Summary Related Articles Check |
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Large Scale Gaussian Mixture Modelling using a Greedy.. - Nunnink (2003) (Correct)
.... of them is a generalisation of the EM algorithm, based on the variational free energy in statistical physics, that justifies several useful variants [11] Also, some improvements aimed at lowering the computational cost by working with groups of data items instead of the items themselves [9] In [1, 6, 10] this method is applied to the k means algorithm) Finally, a greedy version of EM was proposed, which deals with the initialisation problems of EM, thus resulting in higher quality models [13, 14] This greedy method, however, is computationally more expensive. In this thesis I will describe an ....
T. Kanungo, D. M. Mount, N. Netanyahu, C. Piatko, R. Silverman, and A. Y. Wu. An e#cient k-means clustering algorithm: Analysis and implementation. IEEE Transactions PAMI, 24:881--892, 2002.
A Variational EM Algorithm for Large-Scale Mixture Modeling - Verbeek, Vlassis, Nunnink (2003) (Correct)
....without the need to set any parameters. However, since for both algorithms the run time per iteration is linear in both the number of data items n and the number of clusters k, their applicability is limited in large scale applications with many data and many clusters. Recently several authors [2, 3, 4, 5] proposed speedups of these algorithms based on analyzing large chunks of data at once to save distance computations. The idea is to use geometrical reasoning to determine that for chunks of data a particular prototype is the closest (k means) or the posterior on mixture components hardly varies ....
T. Kanungo, D. M. Mount, N. Netanyahu, C. Piatko, R. Silverman, and A. Y. Wu. An efficient k-means clustering algorithm: Analysis and implementation. IEEE Transactions PAMI, 24:881--892, 2002.
The Effectiveness of Lloyd-Type Methods for the k-Means.. - Rafail Ostrovsky Rafail Self-citation (K-) (Correct)
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T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu. An efficient k-means clustering algorithm: Analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell., 24:881--892, 2002.
A Local Search Approximation Algorithm for k-Means.. - Kanungo, Mount.. (2003) (2 citations) Self-citation (Kanungo Mount Netanyahu Piatko Silverman K-) (Correct)
....convergence criterion is satisfied. It can be shown that Lloyd s algorithm eventually converges to a locally optimal solution [38] Computing nearest neighbors is the most expensive step in Lloyd s algorithm, but a number of practical implementations of this algorithm have been discovered recently [2, 24, 35, 36, 37]. x y Data points Optimal centers Heuristic centers z Fig. 1: Lloyd s algorithm can produce an arbitrarily high approximation ratio. Unfortunately, it is easy to construct situations in which Lloyd s algorithm converges to a local minimum that is arbitrarily bad compared to the optimal ....
....of modifying the set of centers and recomputing distortions is called a stage. We measured convergence rates by tracking the lowest distortion encountered as a function of the number of stages executed. We also computed the average CPU time per stage. We use the filtering algorithm from [24] for computing distortions for all the heuristics. The results in each case were averaged over five trials having different random data points (for the synthetic examples) and different random initial centers. We ran the swap heuristic for p # 1,2 swaps. Because they lack a consistent ....
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T. Kanungo, D. M. Mount, N. S. Netanyahu, C. Piatko, R. Silverman, and A. Y. Wu. An efficient k-means clustering algorithm: Analysis and implementation. IEEE Trans. Patt. Anal. Mach. Intell., 24, 2002. (To appear).
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T. Kanungo, D. M. Mount, N. Netanyahu, C. Piatko, R. Silverman, , and A. Y. Wu. 2002. An Efficient K-Means Clustering Algorithm: Analysis and Implementation.
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T. Kanungo, D. M. Mount, N. Netanyahu, C. Piatko, R. Silverman, and A. Y. Wu, An efficient k-means clustering algorithm: Analysis and implementation, IEEE Trans. Pattern Analysis and Machine Intelligence, 24 (2002), 881-892.
The ITK Software Guide - Ibáñez, Schroeder, Ng, .. (2003) (Correct)
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Tapas Kanungo, David M. Mount, Nathan S. Netanyahu, Christine Piatko, Ruth Silverman, and Angela Y. Wu. An efficient k-means clustering algorithm: Analysis and implementation. 10.1.6, 10.3.1
Thesis Proposal: High-quality automatic data clustering - Hamerly (2002) (Correct)
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Tapas Kanungo, David M. Mount, Nathan S. Netanyahu, Christine D. Piatko, Ruth Silverman, and Angela Y. Wu. An ecient k-means clustering algorithm: Analysis and implementation. 24(7):881-892, 2002.
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