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SIGMOD
2004
ACM
144views Database» more  SIGMOD 2004»
14 years 5 months ago
Diamond in the Rough: Finding Hierarchical Heavy Hitters in Multi-Dimensional Data
Data items archived in data warehouses or those that arrive online as streams typically have attributes which take values from multiple hierarchies (e.g., time and geographic loca...
Graham Cormode, Flip Korn, S. Muthukrishnan, Dives...
CACM
2008
52views more  CACM 2008»
13 years 5 months ago
Finding diamonds in the rough
Kirk L. Kroeker
IEEEVAST
2010
13 years 5 days ago
Diamonds in the rough: Social media visual analytics for journalistic inquiry
Journalists increasingly turn to social media sources such as Facebook or Twitter to support their coverage of various news events. For large-scale events such as televised debate...
Nicholas Diakopoulos, Mor Naaman, Funda Kivran-Swa...
ICIP
2001
IEEE
14 years 7 months ago
Adaptive motion search with elastic diamond for MPEG-4 video coding
Video coding is a complex process, comprising a combination of spatial, temporal and statistical data reduction techniques. Of these techniques, motion estimation taking advantage...
Weiguo Zheng, Ishfaq Ahmad, Ming Lei Liou
RSFDGRC
2005
Springer
190views Data Mining» more  RSFDGRC 2005»
13 years 10 months ago
Finding Rough Set Reducts with SAT
Abstract. Feature selection refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine...
Richard Jensen, Qiang Shen, Andrew Tuson