Using Cluster Similarity to Detect Natural Cluster Hierarchies

2007 ◽  
Author(s):  
GuangHui Yan ◽  
ZhanHuai Li
Keyword(s):  
Natural Cluster

J2410101 The Effect of Corona Natural Cluster Ion Produced using Renard Method on Air Cleaning of Life Space and Human Body

2014 ◽  
Vol 2014 (0) ◽  
pp. _J2410101--_J2410101-
Author(s):  
Takayoshi HAYAKAWA ◽  
Takashi HONMA ◽  
Tsutomu UCHIDA ◽  
Satoshi OKAJIMA
Keyword(s):  
Human Body ◽  
Life Space ◽  
Air Cleaning ◽  
Natural Cluster ◽  
Cluster Ion

scholarly journals Finding Natural Cluster Hierarchies Based on MultiFractal

2008 ◽  
Vol 19 (6) ◽  
pp. 1283-1300 ◽  
Author(s):  
Guang-Hui YAN
Keyword(s):  
Natural Cluster

scholarly journals Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters

2009 ◽  
Vol 6 (1) ◽  
pp. 29-123 ◽  
Author(s):  
Jure Leskovec ◽  
Kevin J. Lang ◽  
Anirban Dasgupta ◽  
Michael W. Mahoney
Keyword(s):  
Community Structure ◽  
Large Networks ◽  
Natural Cluster

scholarly journals Automated unsupervised authorship analysis using evidence accumulation clustering

2011 ◽  
Vol 19 (1) ◽  
pp. 95-120 ◽  
Author(s):  
ROBERT LAYTON ◽  
PAUL WATTERS ◽  
RICHARD DAZELEY
Keyword(s):  
Strong Correlation ◽  
Classification Task ◽  
Cluster Ensemble ◽  
Evidence Accumulation ◽  
Related Information ◽  
Natural Cluster ◽  
Unsupervised Methods ◽  
Authorship Analysis ◽  
Extract Information

AbstractAuthorship Analysis aims to extract information about the authorship of documents from features within those documents. Typically, this is performed as a classification task with the aim of identifying the author of a document, given a set of documents of known authorship. Alternatively, unsupervised methods have been developed primarily as visualisation tools to assist the manual discovery of clusters of authorship within a corpus by analysts. However, there is a need in many fields for more sophisticated unsupervised methods to automate the discovery, profiling and organisation of related information through clustering of documents by authorship. An automated and unsupervised methodology for clustering documents by authorship is proposed in this paper. The methodology is named NUANCE, for n-gram Unsupervised Automated Natural Cluster Ensemble. Testing indicates that the derived clusters have a strong correlation to the true authorship of unseen documents.

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