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» A Method for Dynamic Clustering of Data
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SIGIR
2002
ACM
15 years 6 months ago
Unsupervised document classification using sequential information maximization
We present a novel sequential clustering algorithm which is motivated by the Information Bottleneck (IB) method. In contrast to the agglomerative IB algorithm, the new sequential ...
Noam Slonim, Nir Friedman, Naftali Tishby
JSW
2007
96views more  JSW 2007»
15 years 6 months ago
IP-based Clustering for Peer-to-Peer Overlays
— The efficiency of overlay networks built on top of the IP network is often threatened by the mismatch between the topologies of the overlay and the underlying IP network, resu...
Piotr Karwaczynski, Jaka Mocnik
ICPR
2002
IEEE
16 years 7 months ago
A Robust Semi-Supervised EM-Based Clustering Algorithm with a Reject Option
In this paper, we address the problem of semisupervision in the framework of parametric clustering by using labeled and unlabeled data together. Clustering algorithms can take adv...
Christophe Saint-Jean, Carl Frélicot
GFKL
2007
Springer
139views Data Mining» more  GFKL 2007»
16 years 23 days ago
The Noise Component in Model-based Cluster Analysis
The so-called noise-component has been introduced by Banfield and Raftery (1993) to improve the robustness of cluster analysis based on the normal mixture model. The idea is to ad...
Christian Hennig, Pietro Coretto
BMCBI
2007
126views more  BMCBI 2007»
15 years 6 months ago
Including probe-level uncertainty in model-based gene expression clustering
Background: Clustering is an important analysis performed on microarray gene expression data since it groups genes which have similar expression patterns and enables the explorati...
Xuejun Liu, Kevin K. Lin, Bogi Andersen, Magnus Ra...