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KDD
2009
ACM
611views Data Mining» more  KDD 2009»
14 years 11 months ago
Fast approximate spectral clustering
Spectral clustering refers to a flexible class of clustering procedures that can produce high-quality clusterings on small data sets but which has limited applicability to large-s...
Donghui Yan, Ling Huang, Michael I. Jordan
KDD
2009
ACM
198views Data Mining» more  KDD 2009»
14 years 11 months ago
Pervasive parallelism in data mining: dataflow solution to co-clustering large and sparse Netflix data
All Netflix Prize algorithms proposed so far are prohibitively costly for large-scale production systems. In this paper, we describe an efficient dataflow implementation of a coll...
Srivatsava Daruru, Nena M. Marin, Matt Walker, Joy...
MOBISYS
2009
ACM
14 years 11 months ago
Leveraging smart phones to reduce mobility footprints
Mobility footprint refers to the size, weight, and energy demand of the hardware that must be carried by a mobile user to be effective at any time and place. The ideal of a zero m...
Stephen Smaldone, Benjamin Gilbert, Nilton Bila, L...
MOBIHOC
2009
ACM
14 years 11 months ago
Energy-efficient capture of stochastic events by global- and local-periodic network coverage
We consider a high density of sensors randomly placed in a geographical area for event monitoring. The monitoring regions of the sensors may have significant overlap, and a subset...
Shibo He, Jiming Chen, David K. Y. Yau, Huanyu Sha...
KDD
2008
ACM
140views Data Mining» more  KDD 2008»
14 years 11 months ago
Semi-supervised approach to rapid and reliable labeling of large data sets
Supervised classification methods have been shown to be very effective for a large number of applications. They require a training data set whose instances are labeled to indicate...
György J. Simon, Vipin Kumar, Zhi-Li Zhang
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