NDLI: A Text Clustering Algorithm Using an Online Clustering Scheme for Initialization

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A Text Clustering Algorithm Using an Online Clustering Scheme for Initialization

Content Provider	ACM Digital Library
Author	Yin, Jianhua Wang, Jianyong
Abstract	In this paper, we propose a text clustering algorithm using an online clustering scheme for initialization called FGSDMM+. FGSDMM+ assumes that there are at most $K_{max}$ clusters in the corpus, and regards these $K_{max}$ potential clusters as one large potential cluster at the beginning. During initialization, FGSDMM+ processes the documents one by one in an online clustering scheme. The first document will choose the potential cluster, and FGSDMM+ will create a new cluster to store this document. Later documents will choose one of the non-empty clusters or the potential cluster with probabilities derived from the Dirichlet multinomial mixture model. Each time a document chooses the potential cluster, FGSDMM+ will create a new cluster to store that document and decrease the probability of later documents choosing the potential cluster. After initialization, FGSDMM+ will run a collapsed Gibbs sampling algorithm several times to obtain the final clustering result. Our extensive experimental study shows that FGSDMM+ can achieve better performance than three other clustering methods on both short and long text datasets.
Starting Page	1995
Ending Page	2004
Page Count	10
File Format	PDF
ISBN	9781450342322
DOI	10.1145/2939672.2939841
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-08-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Dirichlet multinomial mixture Text clustering Gibbs sampling
Content Type	Text
Resource Type	Article

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