2013 MiningEvolutionaryMultiBranchTr

• (Wang et al., 2013) ⇒ Xiting Wang, Shixia Liu, Yangqiu Song, and Baining Guo. (2013). “Mining Evolutionary Multi-branch Trees from Text Streams.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487603

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Mining+Evolutionary+Multi-branch+Trees+from+Text+Streams
• http://dl.acm.org/citation.cfm?id=2487575.2487603&preflayout=flat#citedby

Quotes

Author Keywords

• Clustering; knowledge acquisition; multi-branch tree; time series analysis; time series data; topic evolution; visualization

Abstract

Understanding topic hierarchies in text streams and their evolution patterns over time is very important in many applications. In this paper, we propose an evolutionary multi-branch tree clustering method for streaming text data. We build evolutionary trees in a Bayesian online filtering framework. The tree construction is formulated as an online posterior estimation problem, which considers both the likelihood of the current tree and conditional prior given the previous tree. We also introduce a constraint model to compute the conditional prior of a tree in the multi-branch setting. Experiments on real world news data demonstrate that our algorithm can better incorporate historical tree information and is more efficient and effective than the traditional evolutionary hierarchical clustering algorithm.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 MiningEvolutionaryMultiBranchTr	Shixia Liu Yangqiu Song Xiting Wang Baining Guo			Mining Evolutionary Multi-branch Trees from Text Streams				10.1145/2487575.2487603		2013