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INFORMATICALT
2008
196views more  INFORMATICALT 2008»
13 years 8 months ago
An Efficient and Sensitive Decision Tree Approach to Mining Concept-Drifting Data Streams
Abstract. Data stream mining has become a novel research topic of growing interest in knowledge discovery. Most proposed algorithms for data stream mining assume that each data blo...
Cheng-Jung Tsai, Chien-I Lee, Wei-Pang Yang
KDD
1994
ACM
118views Data Mining» more  KDD 1994»
14 years 26 days ago
Discovering Informative Patterns and Data Cleaning
Wepresent a methodfor discovering informative patterns from data. With this method,large databases can be reducedto only a few representative data entries. Ourframeworkencompasses...
Isabelle Guyon, Nada Matic, Vladimir Vapnik
ICDM
2010
IEEE
99views Data Mining» more  ICDM 2010»
13 years 6 months ago
A System for Mining Temporal Physiological Data Streams for Advanced Prognostic Decision Support
We present a mining system that can predict the future health status of the patient using the temporal trajectories of health status of a set of similar patients. The main noveltie...
Jimeng Sun, Daby Sow, Jianying Hu, Shahram Ebadoll...
PAKDD
2004
ACM
137views Data Mining» more  PAKDD 2004»
14 years 2 months ago
Fast and Light Boosting for Adaptive Mining of Data Streams
Supporting continuous mining queries on data streams requires algorithms that (i) are fast, (ii) make light demands on memory resources, and (iii) are easily to adapt to concept dr...
Fang Chu, Carlo Zaniolo
SDM
2010
SIAM
144views Data Mining» more  SDM 2010»
13 years 10 months ago
A Probabilistic Framework to Learn from Multiple Annotators with Time-Varying Accuracy
This paper addresses the challenging problem of learning from multiple annotators whose labeling accuracy (reliability) differs and varies over time. We propose a framework based ...
Pinar Donmez, Jaime G. Carbonell, Jeff Schneider