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Graph-Theoretic Techniques for Web Content Mining (Machine Perception and Artificial Intelligence) (Series in Machine Perception and Artificial Intelligence) [Hardcover]

By A. Schenker (Author)
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Item description for Graph-Theoretic Techniques for Web Content Mining (Machine Perception and Artificial Intelligence) (Series in Machine Perception and Artificial Intelligence) by A. Schenker...

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.



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Item Specifications...


Studio: World Scientific Publishing Company
Pages   248
Est. Packaging Dimensions:   Length: 8.98" Width: 6.22" Height: 0.87"
Weight:   1.1 lbs.
Binding  Hardcover
Publisher   World Scientific Publishing Company
ISBN  9812563393  
ISBN13  9789812563392  


Availability  0 units.


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Product Categories

1Books > Special Features > New & Used Textbooks
2Books > Subjects > Computers & Internet > Computer Science > Artificial Intelligence > General
3Books > Subjects > Computers & Internet > Computer Science > Artificial Intelligence > Theory of Computing
4Books > Subjects > Computers & Internet > Computer Science > Software Engineering > Information Systems
5Books > Subjects > Computers & Internet > Databases > Data Storage & Management > Data Mining
6Books > Subjects > Computers & Internet > General
7Books > Subjects > Computers & Internet > Programming > Algorithms > General
8Books > Subjects > Computers & Internet > Programming > Algorithms
9Books > Subjects > Computers & Internet > Programming > General
10Books > Subjects > Professional & Technical > Professional Science > Mathematics > Pure Mathematics > Combinatorics
11Books > Subjects > Science > Mathematics > General
12Books > Subjects > Science > Mathematics > Pure Mathematics > Combinatorics



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