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» Approximation algorithms for projective clustering
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ICALP
2009
Springer
16 years 6 months ago
Correlation Clustering Revisited: The "True" Cost of Error Minimization Problems
Correlation Clustering was defined by Bansal, Blum, and Chawla as the problem of clustering a set of elements based on a possibly inconsistent binary similarity function between e...
Nir Ailon, Edo Liberty
SSD
2005
Springer
173views Database» more  SSD 2005»
16 years 3 days ago
On Discovering Moving Clusters in Spatio-temporal Data
A moving cluster is defined by a set of objects that move close to each other for a long time interval. Real-life examples are a group of migrating animals, a convoy of cars movin...
Panos Kalnis, Nikos Mamoulis, Spiridon Bakiras
ICDM
2008
IEEE
121views Data Mining» more  ICDM 2008»
16 years 1 months ago
Unifying Unknown Nodes in the Internet Graph Using Semisupervised Spectral Clustering
Most research on Internet topology is based on active measurement methods. A major difficulty in using these tools is that one comes across many unresponsive routers. Different m...
Anat Almog, Jacob Goldberger, Yuval Shavitt
ICPR
2010
IEEE
15 years 4 months ago
Performance Evaluation of Automatic Feature Discovery Focused within Error Clusters
We report performance evaluation of our automatic feature discovery method on the publicly available Gisette dataset: a set of 29 features discovered by our method ranks 129 among...
Sui-Yu Wang, Henry S. Baird
DATE
2004
IEEE
108views Hardware» more  DATE 2004»
15 years 10 months ago
Poor Man's TBR: A Simple Model Reduction Scheme
This paper presents a model reduction algorithm motivated by a connection between frequency domain projection methods and approximation of truncated balanced realizations. The met...
Joel R. Phillips, Luis Miguel Silveira