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record xmlns http:www.loc.govMARC21slim xmlns:xsi http:www.w3.org2001XMLSchemainstance xsi:schemaLocation http:www.loc.govstandardsmarcxmlschemaMARC21slim.xsd leader nam Ka controlfield tag 001 001441470 003 fts 006 med 007 cr mnuuuuuu 008 031203s2003 flua sbm s0000 eng d datafield ind1 8 ind2 024 subfield code a E14SFE0000143 035 (OCoLC)53961827 9 AJM5910 b SE SFE0000143 040 FHM c FHM 090 TK7885 1 100 Schenker, Adam. 0 245 Graphtheoretic techniques for web content mining h [electronic resource] / by Adam Schenker. 260 [Tampa, Fla.] : University of South Florida, 2003. 502 Thesis (Ph.D.)University of South Florida, 2003. 500 Includes vita. 504 Includes bibliographical references. 516 Text (Electronic thesis) in PDF format. 538 System requirements: World Wide Web browser and PDF reader. Mode of access: World Wide Web. Title from PDF of title page. Document formatted into pages; contains 145 pages. 520 ABSTRACT: In this dissertation we introduce several novel techniques for performing data mining on web documents which utilize graph representations of document content. Graphs are more robust than typical vector representations as they can model structural information that is usually lost when converting the original web document content to a vector representation. For example, we can capture information such as the location, order and proximity of term occurrence, which is discarded under the standard document vector representation models. Many machine learning methods rely on distance computations, centroid calculations, and other numerical techniques. Thus many of these methods have not been applied to data represented by graphs since no suitable graphtheoretical concepts were previously available. We introduce the novel Graph Hierarchy Construction Algorithm (GHCA), which performs topicoriented hierarchical clustering of web search results modeled using graphs. The system we created around this new algorithm and its prior version is compared with similar web search clustering systems to gauge its usefulness. An important advantage of this approach over conventional web search systems is that the results are better organized and more easily browsed by users. Next we present extensions to classical machine learning algorithms, such as the kmeans clustering algorithm and the kNearest Neighbors classification algorithm, which allows the use of graphs as fundamental data items instead of vectors. We perform experiments comparing the performance of the new graphbased methods to the traditional vectorbased methods for three web document collections. Our experimental results show an improvement for the graph approaches over the vector approaches for both clustering and classification of web documents. An important advantage of the graph representations we propose is that they allow the computation of graph similarity in polynomial time; usually the determination of graph similarity with the techniques we use is an NPComplete problem. In fact, there are some cases where the execution time of the graphoriented approach was faster than the vector approaches. 590 Adviser: Kandel, Abraham 653 graph similarity. graph distance. clustering. machine learning. classification. 690 Dissertations, Academic z USF x Computer Science and Engineering Doctoral. 773 t USF Electronic Theses and Dissertations. 4 856 u http://digital.lib.usf.edu/?e14.143 