View in EDS PDF Full Text

Graph-theoretic Techniques For Web Content Mining

Saved in:
Bibliographic Details
Title: Graph-theoretic Techniques For Web Content Mining
Description: This book describes exciting new opportunities for utilizing robust graph representations of data with common machine learning algorithms. Graphs can model additional information which is often not present in commonly used data representations, such as vectors. Through the use of graph distance — a relatively new approach for determining graph similarity — the authors show how well-known algorithms, such as k-means clustering and k-nearest neighbors classification, can be easily extended to work with graphs instead of vectors. This allows for the utilization of additional information found in graph representations, while at the same time employing well-known, proven algorithms.To demonstrate and investigate these novel techniques, the authors have selected the domain of web content mining, which involves the clustering and classification of web documents based on their textual substance. Several methods of representing web document content by graphs are introduced; an interesting feature of these representations is that they allow for a polynomial time distance computation, something which is typically an NP-complete problem when using graphs. Experimental results are reported for both clustering and classification in three web document collections using a variety of graph representations, distance measures, and algorithm parameters.In addition, this book describes several other related topics, many of which provide excellent starting points for researchers and students interested in exploring this new area of machine learning further. These topics include creating graph-based multiple classifier ensembles through random node selection and visualization of graph-based data using multidimensional scaling.
Authors: Adam Schenker, Horst Bunke, Mark Last, Abraham Kandel
Resource Type: eBook.
Subjects: Algorithms, Graph theory--Data processing, Data mining, Multidimensional scaling, Computer algorithms
Categories: COMPUTERS / Artificial Intelligence / General
Database: eBook Collection (EBSCOhost)
FullText Links:
  – Type: ebook-pdf
Text:
  Availability: 0
Header DbId: nlebk
DbLabel: eBook Collection (EBSCOhost)
An: 161369
RelevancyScore: 998
AccessLevel: 6
PubType: eBook
PubTypeId: ebook
PreciseRelevancyScore: 998.4384765625
IllustrationInfo
ImageInfo – Size: thumb
  Target: https://rps2images.ebscohost.com/rpsweb/othumb?id=NL$161369$PDF&s=r
– Size: medium
  Target: https://rps2images.ebscohost.com/rpsweb/othumb?id=NL$161369$PDF&s=d
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Graph-theoretic Techniques For Web Content Mining
– Name: Abstract
  Label: Description
  Group: Ab
  Data: This book describes exciting new opportunities for utilizing robust graph representations of data with common machine learning algorithms. Graphs can model additional information which is often not present in commonly used data representations, such as vectors. Through the use of graph distance — a relatively new approach for determining graph similarity — the authors show how well-known algorithms, such as k-means clustering and k-nearest neighbors classification, can be easily extended to work with graphs instead of vectors. This allows for the utilization of additional information found in graph representations, while at the same time employing well-known, proven algorithms.To demonstrate and investigate these novel techniques, the authors have selected the domain of web content mining, which involves the clustering and classification of web documents based on their textual substance. Several methods of representing web document content by graphs are introduced; an interesting feature of these representations is that they allow for a polynomial time distance computation, something which is typically an NP-complete problem when using graphs. Experimental results are reported for both clustering and classification in three web document collections using a variety of graph representations, distance measures, and algorithm parameters.In addition, this book describes several other related topics, many of which provide excellent starting points for researchers and students interested in exploring this new area of machine learning further. These topics include creating graph-based multiple classifier ensembles through random node selection and visualization of graph-based data using multidimensional scaling.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Adam+Schenker%22">Adam Schenker</searchLink><br /><searchLink fieldCode="AR" term="%22Horst+Bunke%22">Horst Bunke</searchLink><br /><searchLink fieldCode="AR" term="%22Mark+Last%22">Mark Last</searchLink><br /><searchLink fieldCode="AR" term="%22Abraham+Kandel%22">Abraham Kandel</searchLink>
– Name: TypePub
  Label: Resource Type
  Group: TypPub
  Data: eBook.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+theory--Data+processing%22">Graph theory--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Multidimensional+scaling%22">Multidimensional scaling</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+algorithms%22">Computer algorithms</searchLink>
– Name: SubjectBISAC
  Label: Categories
  Group: Su
  Data: <searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Artificial+Intelligence+%2F+General%22">COMPUTERS / Artificial Intelligence / General</searchLink>
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=161369
RecordInfo BibRecord:
  BibEntity:
    Classifications:
      – Code: 006.312
        Scheme: ddc
        Type: prePub
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Graph theory--Data processing
        Type: general
      – SubjectFull: Data mining
        Type: general
      – SubjectFull: Multidimensional scaling
        Type: general
      – SubjectFull: Computer algorithms
        Type: general
    Titles:
      – TitleFull: Graph-theoretic Techniques For Web Content Mining
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Adam Schenker
      – PersonEntity:
          Name:
            NameFull: Horst Bunke
      – PersonEntity:
          Name:
            NameFull: Mark Last
      – PersonEntity:
          Name:
            NameFull: Abraham Kandel
      – PersonEntity:
          Name:
            NameFull: Adam Schenker
      – PersonEntity:
          Name:
            NameFull: Horst Bunke
      – PersonEntity:
          Name:
            NameFull: Mark Last
      – PersonEntity:
          Name:
            NameFull: Abraham Kandel
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2005
            – D: 04
              M: 02
              Type: profile
              Y: 2014
          Identifiers:
            – Type: isbn-print
              Value: 9789812563392
            – Type: isbn-electronic
              Value: 9789812569455
          Numbering:
            – Type: volume
              Value: 00062
          Titles:
            – TitleFull: Graph-theoretic Techniques For Web Content Mining
              Type: main
ResultId 1