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» Structure induction by lossless graph compression
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DCC
2007
IEEE
14 years 4 months ago
Structure induction by lossless graph compression
This work is motivated by the necessity to automate the discovery of structure in vast and evergrowing collection of relational data commonly represented as graphs, for example ge...
Leonid Peshkin
TIT
2010
138views Education» more  TIT 2010»
12 years 11 months ago
Functional compression through graph coloring
Motivated by applications to sensor networks and privacy preserving databases, we consider the problem of functional compression. The objective is to separately compress possibly c...
Vishal Doshi, Devavrat Shah, Muriel Médard,...
SAC
2002
ACM
13 years 4 months ago
Decision tree classification of spatial data streams using Peano Count Trees
Many organizations have large quantities of spatial data collected in various application areas, including remote sensing, geographical information systems (GIS), astronomy, compu...
Qiang Ding, Qin Ding, William Perrizo
VLDB
2004
ACM
163views Database» more  VLDB 2004»
13 years 10 months ago
Compressing Large Boolean Matrices using Reordering Techniques
Large boolean matrices are a basic representational unit in a variety of applications, with some notable examples being interactive visualization systems, mining large graph struc...
David S. Johnson, Shankar Krishnan, Jatin Chhugani...
JMLR
2010
134views more  JMLR 2010»
12 years 11 months ago
Inference of Graphical Causal Models: Representing the Meaningful Information of Probability Distributions
This paper studies the feasibility and interpretation of learning the causal structure from observational data with the principles behind the Kolmogorov Minimal Sufficient Statist...
Jan Lemeire, Kris Steenhaut