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» Discovering Informative Patterns and Data Cleaning
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CIKM
2010
Springer
13 years 5 months ago
Mining topic-level influence in heterogeneous networks
Influence is a complex and subtle force that governs the dynamics of social networks as well as the behaviors of involved users. Understanding influence can benefit various applic...
Lu Liu, Jie Tang, Jiawei Han, Meng Jiang, Shiqiang...
FAST
2010
13 years 9 months ago
Discovery of Application Workloads from Network File Traces
An understanding of application I/O access patterns is useful in several situations. First, gaining insight into what applications are doing with their data at a semantic level he...
Neeraja J. Yadwadkar, Chiranjib Bhattacharyya, K. ...
KDD
2010
ACM
249views Data Mining» more  KDD 2010»
13 years 9 months ago
Semi-supervised sparse metric learning using alternating linearization optimization
In plenty of scenarios, data can be represented as vectors mathematically abstracted as points in a Euclidean space. Because a great number of machine learning and data mining app...
Wei Liu, Shiqian Ma, Dacheng Tao, Jianzhuang Liu, ...
KDD
2005
ACM
153views Data Mining» more  KDD 2005»
14 years 7 months ago
Using retrieval measures to assess similarity in mining dynamic web clickstreams
While scalable data mining methods are expected to cope with massive Web data, coping with evolving trends in noisy data in a continuous fashion, and without any unnecessary stopp...
Olfa Nasraoui, Cesar Cardona, Carlos Rojas
CIVR
2008
Springer
166views Image Analysis» more  CIVR 2008»
13 years 9 months ago
Non-negative matrix factorisation for object class discovery and image auto-annotation
In information retrieval, sub-space techniques are usually used to reveal the latent semantic structure of a data-set by projecting it to a low dimensional space. Non-negative mat...
Jiayu Tang, Paul H. Lewis