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» Topic Evolution in a Stream of Documents
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KDD
2009
ACM
191views Data Mining» more  KDD 2009»
14 years 5 months ago
Efficient methods for topic model inference on streaming document collections
Topic models provide a powerful tool for analyzing large text collections by representing high dimensional data in a low dimensional subspace. Fitting a topic model given a set of...
Limin Yao, David M. Mimno, Andrew McCallum
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
SDM
2007
SIAM
187views Data Mining» more  SDM 2007»
13 years 6 months ago
Topic Models over Text Streams: A Study of Batch and Online Unsupervised Learning
Topic modeling techniques have widespread use in text data mining applications. Some applications use batch models, which perform clustering on the document collection in aggregat...
Arindam Banerjee, Sugato Basu
GRC
2008
IEEE
13 years 4 months ago
MovStream: An Efficient Algorithm for Monitoring Clusters Evolving in Data Streams
Monitoring cluster evolution in data streams is a major research topic in data streams mining. Previous clustering methods for evolving data streams focus on global clustering res...
Liang Tang, Chang-jie Tang, Lei Duan, Chuan Li, Ye...
ECIR
2009
Springer
14 years 1 months ago
Topic and Trend Detection in Text Collections Using Latent Dirichlet Allocation
Algorithms that enable the process of automatically mining distinct topics in document collections have become increasingly important due to their applications in many fields and ...
Levent Bolelli, Seyda Ertekin, C. Lee Giles