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KDD
2008
ACM
259views Data Mining» more  KDD 2008»
16 years 4 months ago
Using ghost edges for classification in sparsely labeled networks
We address the problem of classification in partially labeled networks (a.k.a. within-network classification) where observed class labels are sparse. Techniques for statistical re...
Brian Gallagher, Hanghang Tong, Tina Eliassi-Rad, ...
KDD
2008
ACM
138views Data Mining» more  KDD 2008»
16 years 4 months ago
Quantitative evaluation of approximate frequent pattern mining algorithms
Traditional association mining algorithms use a strict definition of support that requires every item in a frequent itemset to occur in each supporting transaction. In real-life d...
Rohit Gupta, Gang Fang, Blayne Field, Michael Stei...
KDD
2007
ACM
191views Data Mining» more  KDD 2007»
16 years 4 months ago
Modeling relationships at multiple scales to improve accuracy of large recommender systems
The collaborative filtering approach to recommender systems predicts user preferences for products or services by learning past useritem relationships. In this work, we propose no...
Robert M. Bell, Yehuda Koren, Chris Volinsky
KDD
2006
ACM
159views Data Mining» more  KDD 2006»
16 years 4 months ago
Global distance-based segmentation of trajectories
This work introduces distance-based criteria for segmentation of object trajectories. Segmentation leads to simplification of the original objects into smaller, less complex primi...
Aris Anagnostopoulos, Michail Vlachos, Marios Hadj...
KDD
2006
ACM
180views Data Mining» more  KDD 2006»
16 years 4 months ago
Learning the unified kernel machines for classification
Kernel machines have been shown as the state-of-the-art learning techniques for classification. In this paper, we propose a novel general framework of learning the Unified Kernel ...
Steven C. H. Hoi, Michael R. Lyu, Edward Y. Chang