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KDD
2007
ACM
176views Data Mining» more  KDD 2007»
14 years 4 months ago
Mining correlated bursty topic patterns from coordinated text streams
Previous work on text mining has almost exclusively focused on a single stream. However, we often have available multiple text streams indexed by the same set of time points (call...
Xuanhui Wang, ChengXiang Zhai, Xiao Hu, Richard Sp...
ECAI
2010
Springer
13 years 2 months ago
From bursty patterns to bursty facts: The effectiveness of temporal text mining for news
Many document collections are by nature dynamic, evolving as the topics or events they describe change. The goal of temporal text mining is to discover bursty patterns and to ident...
Ilija Subasic, Bettina Berendt
SDM
2007
SIAM
177views Data Mining» more  SDM 2007»
13 years 5 months ago
Bursty Feature Representation for Clustering Text Streams
Text representation plays a crucial role in classical text mining, where the primary focus was on static text. Nevertheless, well-studied static text representations including TFI...
Qi He, Kuiyu Chang, Ee-Peng Lim, Jun Zhang
ACL
2012
11 years 6 months ago
Finding Bursty Topics from Microblogs
Microblogs such as Twitter reflect the general public’s reactions to major events. Bursty topics from microblogs reveal what events have attracted the most online attention. Al...
Qiming Diao, Jing Jiang, Feida Zhu, Ee-Peng Lim
WSDM
2009
ACM
136views Data Mining» more  WSDM 2009»
13 years 11 months ago
Mining common topics from multiple asynchronous text streams
Text streams are becoming more and more ubiquitous, in the forms of news feeds, weblog archives and so on, which result in a large volume of data. An effective way to explore the...
Xiang Wang 0002, Kai Zhang, Xiaoming Jin, Dou Shen