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» Distance Functions to Detect Changes in Data Streams
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KDD
2003
ACM
214views Data Mining» more  KDD 2003»
14 years 7 months ago
Adaptive duplicate detection using learnable string similarity measures
The problem of identifying approximately duplicate records in databases is an essential step for data cleaning and data integration processes. Most existing approaches have relied...
Mikhail Bilenko, Raymond J. Mooney
MMM
2007
Springer
123views Multimedia» more  MMM 2007»
14 years 1 months ago
Tamper Proofing 3D Motion Data Streams
This paper presents a fragile watermarking technique to tamper proof (Mocap) motion capture data. The technique visualizes 3D Mocap data as a series of cluster of points. Watermark...
Parag Agarwal, Balakrishnan Prabhakaran
ICTAI
2007
IEEE
14 years 1 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...
DAWAK
2008
Springer
13 years 9 months ago
Mining Multidimensional Sequential Patterns over Data Streams
Sequential pattern mining is an active field in the domain of knowledge discovery and has been widely studied for over a decade by data mining researchers. More and more, with the ...
Chedy Raïssi, Marc Plantevit
MM
2005
ACM
134views Multimedia» more  MM 2005»
14 years 29 days ago
Formulating context-dependent similarity functions
Tasks of information retrieval depend on a good distance function for measuring similarity between data instances. The most effective distance function must be formulated in a con...
Gang Wu, Edward Y. Chang, Navneet Panda