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» Mining data streams with periodically changing distributions
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ICDM
2007
IEEE
301views Data Mining» more  ICDM 2007»
13 years 10 months ago
Stream Event Detection: A Unified Framework for Mining Outlier, Change and Burst Simultaneously over Data Stream
Event detection is one of the most important issues of event processing system, especially Complex Event Processing (CEP). Outlier event, change event and burst event are three ty...
Zhijian Yuan, Kai Du, Yan Jia, Jiajia Miao
INFORMATICALT
2008
196views more  INFORMATICALT 2008»
13 years 6 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
ICDM
2007
IEEE
158views Data Mining» more  ICDM 2007»
14 years 20 days ago
On Appropriate Assumptions to Mine Data Streams: Analysis and Practice
Recent years have witnessed an increasing number of studies in stream mining, which aim at building an accurate model for continuously arriving data. Somehow most existing work ma...
Jing Gao, Wei Fan, Jiawei Han
DASFAA
2008
IEEE
149views Database» more  DASFAA 2008»
13 years 7 months ago
A Test Paradigm for Detecting Changes in Transactional Data Streams
A pattern is considered useful if it can be used to help a person to achieve his goal. Mining data streams for useful patterns is important in many applications. However, data stre...
Willie Ng, Manoranjan Dash
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...