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» Scalable Discovery of Best Clusters on Large Graphs
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NIPS
2008
13 years 9 months ago
Online Prediction on Large Diameter Graphs
We continue our study of online prediction of the labelling of a graph. We show a fundamental limitation of Laplacian-based algorithms: if the graph has a large diameter then the ...
Mark Herbster, Guy Lever, Massimiliano Pontil
CLUSTER
2006
IEEE
14 years 1 months ago
MSSG: A Framework for Massive-Scale Semantic Graphs
This paper presents a middleware framework for storing, accessing and analyzing massive-scale semantic graphs. The framework, MSSG, targets scale-free semantic graphs with O(1012 ...
Timothy D. R. Hartley, Ümit V. Çataly&...
ICDM
2010
IEEE
230views Data Mining» more  ICDM 2010»
13 years 5 months ago
Clustering Large Attributed Graphs: An Efficient Incremental Approach
In recent years, many networks have become available for analysis, including social networks, sensor networks, biological networks, etc. Graph clustering has shown its effectivenes...
Yang Zhou, Hong Cheng, Jeffrey Xu Yu
BMCBI
2010
164views more  BMCBI 2010»
13 years 4 months ago
Merged consensus clustering to assess and improve class discovery with microarray data
Background: One of the most commonly performed tasks when analysing high throughput gene expression data is to use clustering methods to classify the data into groups. There are a...
T. Ian Simpson, J. Douglas Armstrong, Andrew P. Ja...
ESCIENCE
2007
IEEE
13 years 11 months ago
A Scalable and Efficient Prefix-Based Lookup Mechanism for Large-Scale Grids
Data sources, storage, computing resources and services are entities on Grids that require mechanisms for publication and lookup. A discovery service relies on efficient lookup to...
Philip Chan, David Abramson