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KDD
2009
ACM
188views Data Mining» more  KDD 2009»
16 years 7 months ago
Mining discrete patterns via binary matrix factorization
Mining discrete patterns in binary data is important for subsampling, compression, and clustering. We consider rankone binary matrix approximations that identify the dominant patt...
Bao-Hong Shen, Shuiwang Ji, Jieping Ye
KDD
2009
ACM
203views Data Mining» more  KDD 2009»
16 years 7 months ago
Characterizing individual communication patterns
The increasing availability of electronic communication data, such as that arising from e-mail exchange, presents social and information scientists with new possibilities for char...
R. Dean Malmgren, Jake M. Hofman, Luis A. N. Amara...
KDD
2009
ACM
198views Data Mining» more  KDD 2009»
16 years 7 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
16 years 7 months ago
SoundSense: scalable sound sensing for people-centric applications on mobile phones
Top end mobile phones include a number of specialized (e.g., accelerometer, compass, GPS) and general purpose sensors (e.g., microphone, camera) that enable new people-centric sen...
Hong Lu, Wei Pan, Nicholas D. Lane, Tanzeem Choudh...
KDD
2008
ACM
181views Data Mining» more  KDD 2008»
16 years 7 months ago
Learning subspace kernels for classification
Kernel methods have been applied successfully in many data mining tasks. Subspace kernel learning was recently proposed to discover an effective low-dimensional subspace of a kern...
Jianhui Chen, Shuiwang Ji, Betul Ceran, Qi Li, Min...
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