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ICDM
2010
IEEE
230views Data Mining» more  ICDM 2010»
13 years 7 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
FSTTCS
2009
Springer
14 years 1 months ago
Bounded Size Graph Clustering with Applications to Stream Processing
We introduce a graph clustering problem motivated by a stream processing application. Input to our problem is an undirected graph with vertex and edge weights. A cluster is a subse...
Rohit Khandekar, Kirsten Hildrum, Sujay Parekh, De...
ICPP
2009
IEEE
14 years 3 months ago
SkipStream: A Clustered Skip Graph Based On-demand Streaming Scheme over Ubiquitous Environments
Providing continuous on-demand streaming services with VCR functionality over ubiquitous environments is challenging due to the stringent QoS requirements of streaming service as ...
Qifeng Yu, Tianyin Xu, Baoliu Ye, Sanglu Lu, Daoxu...
IM
2007
13 years 9 months ago
Using PageRank to Locally Partition a Graph
A local graph partitioning algorithm finds a cut near a specified starting vertex, with a running time that depends largely on the size of the small side of the cut, rather than...
Reid Andersen, Fan R. K. Chung, Kevin J. Lang
MICCAI
2001
Springer
14 years 1 months ago
Segmentation of Dynamic N-D Data Sets via Graph Cuts Using Markov Models
Abstract. This paper describes a new segmentation technique for multidimensional dynamic data. One example of such data is a perfusion sequence where a number of 3D MRI volumes sho...
Yuri Boykov, Vivian S. Lee, Henry Rusinek, Ravi Ba...