Share
Graph-Theoretic Techniques for Web Content Mining (in English)
Mark Last
(Author)
·
Horst Bunke
(Author)
·
Adam Schenker
(Author)
·
World Scientific Publishing Company
· Hardcover
Graph-Theoretic Techniques for Web Content Mining (in English) - Schenker, Adam ; Bunke, Horst ; Last, Mark
$ 201.81
$ 336.36
You save: $ 134.54
Choose the list to add your product or create one New List
✓ Product added successfully to the Wishlist.
Go to My Wishlists
Origin: United Kingdom
(Import costs included in the price)
It will be shipped from our warehouse between
Friday, August 02 and
Tuesday, August 13.
You will receive it anywhere in United States between 1 and 3 business days after shipment.
Synopsis "Graph-Theoretic Techniques for Web Content Mining (in English)"
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.
- 0% (0)
- 0% (0)
- 0% (0)
- 0% (0)
- 0% (0)
All books in our catalog are Original.
The book is written in English.
The binding of this edition is Hardcover.
✓ Producto agregado correctamente al carro, Ir a Pagar.
