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» Approximate mining of frequent patterns on streams
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KDD
2006
ACM
129views Data Mining» more  KDD 2006»
14 years 6 months ago
Suppressing model overfitting in mining concept-drifting data streams
Mining data streams of changing class distributions is important for real-time business decision support. The stream classifier must evolve to reflect the current class distributi...
Haixun Wang, Jian Yin, Jian Pei, Philip S. Yu, Jef...
SDM
2009
SIAM
164views Data Mining» more  SDM 2009»
14 years 3 months ago
Time-Decayed Correlated Aggregates over Data Streams.
Data stream analysis frequently relies on identifying correlations and posing conditional queries on the data after it has been seen. Correlated aggregates form an important examp...
Graham Cormode, Srikanta Tirthapura, Bojian Xu
KAIS
2010
139views more  KAIS 2010»
13 years 4 months ago
Periodic subgraph mining in dynamic networks
In systems of interacting entities such as social networks, interactions that occur regularly typically correspond to significant, yet often infrequent and hard to detect, interact...
Mayank Lahiri, Tanya Y. Berger-Wolf
AUSDM
2008
Springer
221views Data Mining» more  AUSDM 2008»
13 years 7 months ago
Mining Medical Specialist Billing Patterns for Health Service Management
This paper presents an application of association rule mining in compliance in the context of health service management. There are approximately 500 million transactions processed...
Yin Shan, David Jeacocke, D. Wayne Murray, Alison ...
ICMCS
2009
IEEE
199views Multimedia» more  ICMCS 2009»
13 years 3 months ago
Association rule mining in multiple, multidimensional time series medical data
Time series pattern mining (TSPM) finds correlations or dependencies in same series or in multiple time series. When the numerous instances of multiple time series data are associ...
Gaurav N. Pradhan, B. Prabhakaran