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KDD
2004
ACM
170views Data Mining» more  KDD 2004»
16 years 7 months ago
Why collective inference improves relational classification
Procedures for collective inference make simultaneous statistical judgments about the same variables for a set of related data instances. For example, collective inference could b...
David Jensen, Jennifer Neville, Brian Gallagher
KDD
2009
ACM
298views Data Mining» more  KDD 2009»
16 years 1 months ago
Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering
One-Class Collaborative Filtering (OCCF) is a task that naturally emerges in recommender system settings. Typical characteristics include: Only positive examples can be observed, ...
Rong Pan, Martin Scholz
ICDM
2009
IEEE
110views Data Mining» more  ICDM 2009»
16 years 1 months ago
Projective Clustering Ensembles
Recent advances in data clustering concern clustering ensembles and projective clustering methods, each addressing different issues in clustering problems. In this paper, we consi...
Francesco Gullo, Carlotta Domeniconi, Andrea Tagar...
ICDM
2008
IEEE
136views Data Mining» more  ICDM 2008»
16 years 1 months ago
Document-Word Co-regularization for Semi-supervised Sentiment Analysis
The goal of sentiment prediction is to automatically identify whether a given piece of text expresses positive or negative opinion towards a topic of interest. One can pose sentim...
Vikas Sindhwani, Prem Melville
ICDM
2008
IEEE
109views Data Mining» more  ICDM 2008»
16 years 1 months ago
Learning by Propagability
In this paper, we present a novel feature extraction framework, called learning by propagability. The whole learning process is driven by the philosophy that the data labels and o...
Bingbing Ni, Shuicheng Yan, Ashraf A. Kassim, Loon...