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ICDE
2003
IEEE
148views Database» more  ICDE 2003»
15 years 8 days ago
Dynamic Clustering of Evolving Streams with a Single Pass
Stream data is common in many applications, e.g., stock quotes, merchandize sales record, system logs, etc.. It is of great importance to analyze these stream data. As one of the ...
Jiong Yang
CN
2006
163views more  CN 2006»
13 years 11 months ago
A framework for mining evolving trends in Web data streams using dynamic learning and retrospective validation
The expanding and dynamic nature of the Web poses enormous challenges to most data mining techniques that try to extract patterns from Web data, such as Web usage and Web content....
Olfa Nasraoui, Carlos Rojas, Cesar Cardona
KDD
2005
ACM
153views Data Mining» more  KDD 2005»
14 years 11 months ago
Using retrieval measures to assess similarity in mining dynamic web clickstreams
While scalable data mining methods are expected to cope with massive Web data, coping with evolving trends in noisy data in a continuous fashion, and without any unnecessary stopp...
Olfa Nasraoui, Cesar Cardona, Carlos Rojas
ESANN
2008
14 years 11 days ago
Parallelizing single patch pass clustering
Clustering algorithms such as k-means, the self-organizing map (SOM), or Neural Gas (NG) constitute popular tools for automated information analysis. Since data sets are becoming l...
Nikolai Alex, Barbara Hammer
FUZZIEEE
2007
IEEE
14 years 5 months ago
Single Pass Fuzzy C Means
— Recently several algorithms for clustering large data sets or streaming data sets have been proposed. Most of them address the crisp case of clustering, which cannot be easily ...
Prodip Hore, Lawrence O. Hall, Dmitry B. Goldgof