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Document Clustering and Social Networks

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Title: Document Clustering and Social Networks
Author(s): Wegman, Edward J.
Subject(s): Cluster analysis, text mining, mixture models, social networks
Abstract: Text Mining has become a specialized offshoot of Data Mining, Information Retrieval, and Natural Language Processing. One of the major tools of this area is the vector space representation of documents. On the other hand, social network analysis has found its mathematical underpinnings primarily in mathematical graph theory. A graph has a dual representation as an adjacency matrix. So-called two-mode social networks have actors of two different types, frequently individuals and organizations. The adjacency matrix for these two-mode social networks has the same structure as the so-called term-document matrices used in text mining. In the talk we discuss these connections and show how these ideas can be exploited in both fields. In particular, methods for block modeling in social network analysis can be used for document clustering.
Issue Date: 2009-06
Citation Info: Presented at the annual meetings of the Classification and Interface Societies, St. Louis, MO, June 2009.
Genre: Presentation / Lecture / Speech
Type: TextVideoImage
Language: English
URI: http://hdl.handle.net/2142/12608
Publication Status: unpublished
Peer Reviewed: not peer reviewed
Sponsor: Army Research Office, Contract W911NF-04-1-0447Army Research Laboratory, Contract W911-NF-07-1-0059National Institute on Alcohol Abuse And Alcoholism, Grant Number F32AA015876Isaac Newton Institute
Rights Information: Copyright © 2009 by Edward J. Wegman
Date Available in IDEALS: 2009-06-23
 

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