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» A clustering method that uses lossy aggregation of data
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ISCC
2006
IEEE
188views Communications» more  ISCC 2006»
14 years 3 months ago
Active Learning Driven Data Acquisition for Sensor Networks
Online monitoring of a physical phenomenon over a geographical area is a popular application of sensor networks. Networks representative of this class of applications are typicall...
Anish Muttreja, Anand Raghunathan, Srivaths Ravi, ...
CVPR
2008
IEEE
14 years 11 months ago
Incremental learning of nonparametric Bayesian mixture models
Clustering is a fundamental task in many vision applications. To date, most clustering algorithms work in a batch setting and training examples must be gathered in a large group b...
Ryan Gomes, Max Welling, Pietro Perona
ECML
2006
Springer
14 years 27 days ago
Unsupervised Multiple-Instance Learning for Functional Profiling of Genomic Data
Multiple-instance learning (MIL) is a popular concept among the AI community to support supervised learning applications in situations where only incomplete knowledge is available....
Corneliu Henegar, Karine Clément, Jean-Dani...
DOLAP
2003
ACM
14 years 2 months ago
Hierarchical dwarfs for the rollup cube
The data cube operator exemplifies two of the most important aspects of OLAP queries: aggregation and dimension hierarchies. In earlier work we presented Dwarf, a highly compress...
Yannis Sismanis, Antonios Deligiannakis, Yannis Ko...
PAKDD
2009
ACM
186views Data Mining» more  PAKDD 2009»
14 years 4 months ago
Pairwise Constrained Clustering for Sparse and High Dimensional Feature Spaces
Abstract. Clustering high dimensional data with sparse features is challenging because pairwise distances between data items are not informative in high dimensional space. To addre...
Su Yan, Hai Wang, Dongwon Lee, C. Lee Giles