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» Data Stream Clustering: Challenges and Issues
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ICDM
2010
IEEE
199views Data Mining» more  ICDM 2010»
13 years 5 months ago
Addressing Concept-Evolution in Concept-Drifting Data Streams
Abstract--The problem of data stream classification is challenging because of many practical aspects associated with efficient processing and temporal behavior of the stream. Two s...
Mohammad M. Masud, Qing Chen, Latifur Khan, Charu ...
CN
2006
163views more  CN 2006»
13 years 7 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
JMLR
2010
130views more  JMLR 2010»
13 years 1 months ago
MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering
Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA is designed to deal...
Albert Bifet, Geoff Holmes, Bernhard Pfahringer, P...
GLOBECOM
2007
IEEE
14 years 1 months ago
Hierarchically Clustered P2P Streaming System
Abstract— Peer-to-peer video streaming has been gaining popularity. However, it is still challenging to manage a P2P system efficiently to support high video playback rate. In t...
Chao Liang, Yang Guo, Yong Liu
DEBS
2009
ACM
13 years 10 months ago
Event-based systems: opportunities and challenges at exascale
Streaming data models have been shown to be useful in many applications requiring high-performance data exchange. Application-level overlay networks are a natural way to realize t...
Greg Eisenhauer, Matthew Wolf, Hasan Abbasi, Karst...