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ICDM
2009
IEEE
167views Data Mining» more  ICDM 2009»
13 years 6 months ago
Self-Adaptive Anytime Stream Clustering
Clustering streaming data requires algorithms which are capable of updating clustering results for the incoming data. As data is constantly arriving, time for processing is limited...
Philipp Kranen, Ira Assent, Corinna Baldauf, Thoma...
KDD
2009
ACM
198views Data Mining» more  KDD 2009»
14 years 9 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...
INFORMATICALT
2008
124views more  INFORMATICALT 2008»
13 years 9 months ago
Hierarchical Adaptive Clustering
This paper studies an adaptive clustering problem. We focus on re-clustering an object set, previously clustered, when the feature set characterizing the objects increases. We prop...
Gabriela Serban, Alina Campan
ICIP
2006
IEEE
14 years 10 months ago
Acoustic Range Image Segmentation by Effective Mean Shift
Image perception in underwater environment is a difficult task for a human operator, and data segmentation becomes a crucial step toward an higher level interpretation and recogni...
Umberto Castellani, Marco Cristani, Vittorio Murin...
ICDM
2009
IEEE
92views Data Mining» more  ICDM 2009»
13 years 6 months ago
Semi-supervised Multi-task Learning with Task Regularizations
Multi-task learning refers to the learning problem of performing inference by jointly considering multiple related tasks. There have already been many research efforts on supervise...
Fei Wang, Xin Wang, Tao Li