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» On the Performance of Ant-based Clustering
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ISAAC
2009
Springer
175views Algorithms» more  ISAAC 2009»
16 years 1 months ago
Worst-Case and Smoothed Analysis of k-Means Clustering with Bregman Divergences
The k-means algorithm is the method of choice for clustering large-scale data sets and it performs exceedingly well in practice. Most of the theoretical work is restricted to the c...
Bodo Manthey, Heiko Röglin
ICDM
2007
IEEE
129views Data Mining» more  ICDM 2007»
16 years 1 months ago
Semi-supervised Clustering Using Bayesian Regularization
Text clustering is most commonly treated as a fully automated task without user supervision. However, we can improve clustering performance using supervision in the form of pairwi...
Zuobing Xu, Ram Akella, Mike Ching, Renjie Tang
IPPS
2007
IEEE
16 years 1 months ago
Towards A Better Understanding of Workload Dynamics on Data-Intensive Clusters and Grids
This paper presents a comprehensive statistical analysis of workloads collected on data-intensive clusters and Grids. The analysis is conducted at different levels, including Virt...
Hui Li, Lex Wolters
ISICA
2007
Springer
16 years 25 days ago
Instant Message Clustering Based on Extended Vector Space Model
Instant intercommunion techniques such as Instant Messaging (IM) are widely popularized. Aiming at such kind of large scale masscommunication media, clustering on its text conte...
Le Wang, Yan Jia, Weihong Han
PAKDD
2007
ACM
152views Data Mining» more  PAKDD 2007»
16 years 25 days ago
Spectral Clustering Based Null Space Linear Discriminant Analysis (SNLDA)
While null space based linear discriminant analysis (NLDA) obtains a good discriminant performance, the ability easily suffers from an implicit assumption of Gaussian model with sa...
Wenxin Yang, Junping Zhang