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» Computing LTS Regression for Large Data Sets
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KDD
2006
ACM
164views Data Mining» more  KDD 2006»
14 years 9 months ago
Sampling from large graphs
Given a huge real graph, how can we derive a representative sample? There are many known algorithms to compute interesting measures (shortest paths, centrality, betweenness, etc.)...
Jure Leskovec, Christos Faloutsos
CLADE
2004
IEEE
14 years 28 days ago
Grid Service for Visualization and Analysis of Remote Fusion Data
Simulations and experiments in the fusion and plasma physics community generate large datasets at remote sites. Visualization and analysis of these datasets are difficult because ...
Svetlana G. Shasharina, Nanbor Wang, John R. Cary
SIGECOM
2010
ACM
164views ECommerce» more  SIGECOM 2010»
14 years 2 months ago
Automated market-making in the large: the gates hillman prediction market
We designed and built the Gates Hillman Prediction Market (GHPM) to predict the opening day of the Gates and Hillman Centers, the new computer science buildings at Carnegie Mellon...
Abraham Othman, Tuomas Sandholm
BMCBI
2008
163views more  BMCBI 2008»
13 years 9 months ago
ProfileGrids as a new visual representation of large multiple sequence alignments: a case study of the RecA protein family
Background: Multiple sequence alignments are a fundamental tool for the comparative analysis of proteins and nucleic acids. However, large data sets are no longer manageable for v...
Alberto I. Roca, Albert E. Almada, Aaron C. Abajia...
CIKM
2009
Springer
13 years 7 months ago
Diverging patterns: discovering significant frequency change dissimilarities in large databases
In this paper, we present a framework for mining diverging patterns, a new type of contrast patterns whose frequency changes significantly differently in two data sets, e.g., it c...
Aijun An, Qian Wan, Jiashu Zhao, Xiangji Huang