R. Kannan, S. Vempala, and A. Vetta. On clusterings { good, bad and spectral. In Proc. of 41st annual Symposium on Foundations of Computer Science, 2001. Home/Search Document Not in Database Summary Related Articles Check |
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....also helpful because it tends to decrease sensitivity to initialization and to avoid outlier clusters (highly under utilized representatives) from forming, and thus has a beneficial regularizing e#ect. In fact, balance is also an important constraint for spectral graph partitioning algorithms [8, 17, 24], which could give completely useless results if the objective function is just the minimum cut instead of a modified minimum cut that favors balanced clusters. Unfortunately, k means type algorithms (including the soft EM variant [6] and BIRCH [37] are increasingly prone to yielding imbalanced ....
....the synthetic dataset suggest that our balanced clustering algorithm might serve well for this purpose. Spectral clustering has been a hot area of clustering research and often reduces to using regular kmeans to search for appropriate clusters in the eigenspace of either the original vector data [17] or the similarity graph constructed from the original data [24] Our balanced k means may be used to replace the regular kmeans and get more balanced and hopefully improved spectral clustering results. 0.8 0.82 0.84 0.86 0.88 0.9 0.92 0.94 0.96 0.98 1 (normalized entropy) regular ....
R. Kannan, S. Vempala, and A. Vetta. On clusterings --- good, bad and spectral. In 41st Annual IEEE Symp. on Foundations of Computer Science (FOCS'00), Redondo Beach, 2000.
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....secondary (i.e. non principal) eigenvectors (or their positive and negative components) being related to secondary (or opposing) communities of web pages. The use of secondary eigenvectors for discovering communities, or for improving the quality of the ranking has been investigated further in [13, 1, 19]. We now present a few simple examples which we feel illustrate the opinion that such secondary eigenvectors sometimes are, but sometimes are not, indicative of secondary communities. In short, there is no simple result either way, regarding these secondary eigenvectors. For the following, we ....
R. Kannan, S. Vempala, and A. Vetta. On clusterings: good, bad and spectral. In Proceedings of the 41st Foundation of Computer Science (FOCS 2000.
Clustering Methods Based on Minimum-Cut Trees - Flake, Tarjan, Tsioutsiouliklis (2002) (Correct)
....times. In this paper we present a new clustering algorithm which is based on maximum ow techniques. Maximum ow algorithms are relatively fast and simple, and have been used in the past for dataclustering (e.g. 18] 3] Diculties in the analysis and in practice have also been pointed out in [11]. The main idea behind maximum ow (or equivalently, minimum cut [4] techniques is to create clusters that have small inter cluster cuts (i.e. between clusters) and relatively large intracluster cuts (i.e. within clusters) This guarantees strong connectedness within the clusters and is also a ....
....w(CnS)g : The conductance of a cluster (C) is the smallest conductance of a cut within the cluster. For a clustering, the conductance is the minimun conductance of its clusters. Both expansion and conductance seem to give very good measures of quality for clusterings and are claimed in [11] to be generally better than simple minimum cuts. We agree with this claim, as it was true for the majority of the data we have experimented with, including that presented in Section 5. The main di erence between expansion and conductance is that expansion treats all nodes as equally important and ....
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R. Kannan, S. Vempala, and A. Vetta. On Clusterings - Good, Bad and Spectral. In IEEE Symposium on Foundations of Computer Science, pages 367-377, 2000.
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....references are given. 6.4 How good are clustering algorithms In a clustering algorithm the objective is to nd a good clustering but a good clustering is not formally de ned. Intuitively the quality of a clustering is assessed by how much similar points are grouped in the same cluster. In [58] the question is posed: how good is the clustering which is produced by a clustering algorithm As already discussed the k median clustering may produce a very bad clustering in case the hidden clusters are far from spherical. e.g. imagine two clusters, one that is a sphere and a second one is ....
R. Kannan, S. Vempala, and A. Vetta. On clusterings - good, bad and spectral. In FOCS, pages 367-377, 2000.
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R. Kannan, S. Vempala, and A. Vetta. On clusterings { good, bad and spectral. In Proc. of 41st annual Symposium on Foundations of Computer Science, 2001.
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R. Kannan, S. Vempala, and A. Veta. On clusterings-good, bad and spectral. In Proceedings of the 41st Annual Symposium on Foundations of Computer Science, Nov 2000.
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R. Kannan, S. Vempala, and A. Vetta. On clusterings --- good, bad and spectral. In 41st Symposium on the Foundations of Computer Science, 2000.
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R. Kannan, S. Vempala, and A. Vetta. On clusterings { good, bad and spectral. In Proc. of 41st annual Symposium on Foundations of Computer Science, 2001.
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R. Kannan, S. Vempala, and A. Vetta. On clusterings --- good, bad and spectral. In Proceedings of the 41st Annual Symposium on the Foundation of Computer Science, pages 367--377, Los Alamitos, CA, USA, 2000. IEEE Computer Society Press.
Experiments on Graph Clustering Algorithms - Brandes, Gaertler, Wagner (2003) (1 citation) (Correct)
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Kannan, R., Vampala, S., Vetta, A.: On Clustering --- Good, Bad and Spectral. In: Foundations of Computer Science 2000. (2000) 367--378
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R. Kannan, S. Vempala, and A. Vetta. On clusterings: Good, bad and spectral. Journal of the ACM, 51(3):497--515, 2004.
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R. Kannan, S. Vempala, and A. Vetta. On clusterings --- good, bad and spectral. In Proceedings of the 41st Annual Symposium on the Foundation of Computer Science, pages 367--377, Los Alamitos, CA, USA, 2000. IEEE Computer Society Press.
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R. Kannan, S. Vempala, and A. Vetta. On clusterings: Good, bad and spectral. Proc. FOCS, pages 367--377, 2000.
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R. Kannan, S. Vempala, and A. Vetta. On clusterings: good, bad and spectral. In Proc. of the 41st Ann. IEEE Symp. on Foundations of Computer Science, 2000.
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R. Kannan, S. Vempala, and A. Vetta. On clusterings: Good, bad and spectral. In Proceedings of the 41st Foundations of Computer Science (FOCS '00), page 367, 2000.
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