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» Info-fuzzy algorithms for mining dynamic data streams
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KDD
2006
ACM
129views Data Mining» more  KDD 2006»
16 years 4 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...
ICDM
2006
IEEE
139views Data Mining» more  ICDM 2006»
15 years 10 months ago
Unsupervised Clustering In Streaming Data
Tools for automatically clustering streaming data are becoming increasingly important as data acquisition technology continues to advance. In this paper we present an extension of...
Dimitris K. Tasoulis, Niall M. Adams, David J. Han...
KDD
2012
ACM
178views Data Mining» more  KDD 2012»
13 years 6 months ago
Mining emerging patterns by streaming feature selection
Building an accurate emerging pattern classifier with a highdimensional dataset is a challenging issue. The problem becomes even more difficult if the whole feature space is unava...
Kui Yu, Wei Ding 0003, Dan A. Simovici, Xindong Wu
VLDB
2006
ACM
190views Database» more  VLDB 2006»
16 years 4 months ago
Online summarization of dynamic time series data
Managing large-scale time series databases has attracted significant attention in the database community recently. Related fundamental problems such as dimensionality reduction, tr...
Ümit Y. Ogras, Hakan Ferhatosmanoglu
ICTAI
2007
IEEE
15 years 10 months ago
An Adaptive Distributed Ensemble Approach to Mine Concept-Drifting Data Streams
An adaptive boosting ensemble algorithm for classifying homogeneous distributed data streams is presented. The method builds an ensemble of classifiers by using Genetic Programmi...
Gianluigi Folino, Clara Pizzuti, Giandomenico Spez...