Please use this identifier to cite or link to this item: http://localhost:8081/xmlui/handle/123456789/11759
Title: CLUSTERING BASED METHOD FOR DISCOVERING EVOLUTIONARY THEME PATTERNS IN A COLLECTION OF TEXT ARTICLES
Authors: Dalai, Mohan Kumar
Keywords: ELECTRONICS AND COMPUTER ENGINEERING;CLUSTERING BASED METHOD;DISCOVERING EVOLUTIONARY THEME;PATTERNS -TEXT ARTICLES
Issue Date: 2007
Abstract: In this thesis work we consider the problem of analyzing the development of a document collection over time without requiring meaningful citation data. Given a collection of time stamped documents, we formulate and explore the following two questions. First, what are the main topics and how do these topics develop over time? Second, what are the documents and who are the authors that are most influential in this process?. We propose methods addressing these questions by taking solely text of the document as input. Because proposed methods use only the text of the documents as input, the methods are applicable to a much wider range of document collections (email, blogs, etc.), most of which lack meaningful citation data. We evaluate our methods on two kinds of data sets one is the documents from the proceedings of the Neural Information Processing Systems (NIPS) conference and the other is collection of news articles. The results show that the methods are effective and that addressing the questions based on the text alone . In fact, the text-based methods sometimes even identify influential papers that are missed by citation analysis.
URI: http://hdl.handle.net/123456789/11759
Other Identifiers: M.Tech
Research Supervisor/ Guide: Singh, Kuldip
metadata.dc.type: M.Tech Dessertation
Appears in Collections:MASTERS' DISSERTATIONS (E & C)

Files in This Item:
File Description SizeFormat 
ECDG13426.pdf4.42 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.