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» Clustering with the Connectivity Kernel
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NIPS
2003
13 years 6 months ago
Clustering with the Connectivity Kernel
Clustering aims at extracting hidden structure in dataset. While the problem of finding compact clusters has been widely studied in the literature, extracting arbitrarily formed ...
Bernd Fischer, Volker Roth, Joachim M. Buhmann
IADIS
2004
13 years 6 months ago
Speculative TCP Connection Admission Using Connection Migration in Cluster-Based Servers
This paper presents speculative TCP connection admission, a mechanism for improving sub-optimal request distribution decisions in cluster-based servers. Overloaded server nodes in...
Vlad Olaru, Walter F. Tichy
NIPS
2007
13 years 6 months ago
Discriminative K-means for Clustering
We present a theoretical study on the discriminative clustering framework, recently proposed for simultaneous subspace selection via linear discriminant analysis (LDA) and cluster...
Jieping Ye, Zheng Zhao, Mingrui Wu
ICML
2005
IEEE
14 years 5 months ago
Semi-supervised graph clustering: a kernel approach
Semi-supervised clustering algorithms aim to improve clustering results using limited supervision. The supervision is generally given as pairwise constraints; such constraints are...
Brian Kulis, Sugato Basu, Inderjit S. Dhillon, Ray...
KDD
2004
ACM
190views Data Mining» more  KDD 2004»
14 years 5 months ago
Kernel k-means: spectral clustering and normalized cuts
Kernel k-means and spectral clustering have both been used to identify clusters that are non-linearly separable in input space. Despite significant research, these methods have re...
Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis