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TNN
2010
155views Management» more  TNN 2010»
13 years 3 months ago
Incorporating the loss function into discriminative clustering of structured outputs
Clustering using the Hilbert Schmidt independence criterion (CLUHSIC) is a recent clustering algorithm that maximizes the dependence between cluster labels and data observations ac...
Wenliang Zhong, Weike Pan, James T. Kwok, Ivor W. ...
BPM
2008
Springer
192views Business» more  BPM 2008»
13 years 10 months ago
Trace Clustering in Process Mining
Process mining has proven to be a valuable tool for analyzing operational process executions based on event logs. Existing techniques perform well on structured processes, but stil...
Minseok Song, Christian W. Günther, Wil M. P....
PVLDB
2008
182views more  PVLDB 2008»
13 years 8 months ago
SCOPE: easy and efficient parallel processing of massive data sets
Companies providing cloud-scale services have an increasing need to store and analyze massive data sets such as search logs and click streams. For cost and performance reasons, pr...
Ronnie Chaiken, Bob Jenkins, Per-Åke Larson,...
BPM
2009
Springer
161views Business» more  BPM 2009»
14 years 3 months ago
Trace Clustering Based on Conserved Patterns: Towards Achieving Better Process Models
Process mining refers to the extraction of process models from event logs. Real-life processes tend to be less structured and more flexible. Traditional process mining algorithms ...
R. P. Jagadeesh Chandra Bose, Wil M. P. van der Aa...
ICML
2007
IEEE
14 years 9 months ago
Multi-task learning for sequential data via iHMMs and the nested Dirichlet process
A new hierarchical nonparametric Bayesian model is proposed for the problem of multitask learning (MTL) with sequential data. Sequential data are typically modeled with a hidden M...
Kai Ni, Lawrence Carin, David B. Dunson