Graph-theoretic techniques for web content mining /
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| Imprint: | [Hackensack], N.J. ; London : World Scientific, 2005. |
|---|---|
| Description: | 1 online resource (xi, 235 pages) : illustrations. |
| Language: | English |
| Series: | Series in machine perception and artificial intelligence ; v. 62 Series in machine perception and artificial intelligence ; v. 62. |
| Subject: | |
| Format: | E-Resource Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/11201035 |
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| 245 | 0 | 0 | |a Graph-theoretic techniques for web content mining / |c Adam Schenker [and others]. |
| 260 | |a [Hackensack], N.J. ; |a London : |b World Scientific, |c 2005. | ||
| 300 | |a 1 online resource (xi, 235 pages) : |b illustrations. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a data file |2 rda | ||
| 490 | 1 | |a Series in machine perception and artificial intelligence ; |v v. 62 | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Cover -- Preface -- Acknowledgements -- Contents -- Chapter 1 Introduction to Web Mining -- 1.1 Overview of Web Mining Methodologies -- 1.2 Traditional Information Retrieval Techniques -- 1.2.1 Vector-based distance measures -- 1.2.2 Special considerations for web documents -- 1.3 Overview of Remaining Chapters -- Chapter 2 Graph Similarity Techniques -- 2.1 Graph and Subgraph Isomorphism -- 2.2 Graph Edit Distance -- 2.3 Maximum Common Subgraph / Minimum Common Supergraph Approach -- 2.4 State Space Search Approach -- 2.5 Probabilistic Approach -- 2.6 Distance Preservation Approach -- 2.7 Relaxation Approaches -- 2.8 Mean and Median of Graphs -- 2.9 Summary -- Chapter 3 Graph Models for Web Documents -- 3.1 Pre-Processing -- 3.2 Graph Representations of Web Documents -- 3.3 Complexity Analysis -- 3.4 Web Document Data Sets -- Chapter 4 Graph-Based Clustering -- 4.1 The Graph-Based k-Means Clustering Algorithm -- 4.2 Clustering Performance Measures -- 4.3 Comparison with Previously Published Results -- 4.4 Comparison of Different Graph-Theoretical Distance Measures and Graph Representations for Graph-Based Clustering -- 4.4.1 Comparison of distance measures -- 4.4.2 Comparison of graph representations -- 4.5 Comparison of Clustering Algorithms -- 4.6 Visualization of Graph Clustering -- 4.7 The Graph-Based Global k-Means Algorithm -- 4.7.1 Global k-means vs. random initialization -- 4.7.2 Optimum number of clusters -- Chapter 5 Graph-Based Classification -- 5.1 The k-Nearest Neighbors Algorithm -- 5.1.1 Traditional method -- 5.1.2 Graph-based approach -- 5.1.3 Experimental results -- 5.2 Graph-Based Multiple Classifier Ensembles -- 5.2.1 Basic algorithm -- 5.2.2 Experimental results -- Chapter 6 The Graph Hierarchy Construction Algorithm for Web Search Clustering -- 6.1 Cluster Hierarchy Construction Algorithm (CHCA) -- 6.1.1 A review of inheritance -- 6.1.2 Brief overview of CHCA -- 6.1.3 CHCA in detail -- 6.1.4 CHCA: An example -- 6.1.5 Examination of CHCA as a clustering method -- 6.2 Application of CHCA to Search Results Processing -- 6.2.1 Asynchronous search -- 6.2.2 Implementation, input preparation and pre-processing -- 6.2.3 Selection of parameters for web search -- 6.3 Examples of Results -- 6.3.1 Comparison with Grouper -- 6.3.2 Comparison with Vivisimo -- 6.4 Graph Hierarchy Construction Algorithm (GHCA) -- 6.4.1 Parameters -- 6.4.2 Graph creation and pre-processing -- 6.4.3 Graph Hierarchy Construction Algorithm (GHCA) -- 6.4.4 GHCA examples -- 6.5 Comments -- Chapter 7 Conclusions and Future Work -- Appendix A Graph Examples -- College of Engineering -- Professional Engineering -- Professional Registration -- Preparation for Engineering -- Student Computer Policy -- Departments and Programs -- Chemical Engineering -- Civil and Environmental Engineering -- Computer Science and Engineering -- Electrical Engineering -- Industrial and Management Systems Engineering -- Mechanical Engineering -- Computer Service (SC) Courses -- College Computing Facilities -- Cooperative Education and Internship Programs -- Army, Air Force & Navy R.O.T.C. For Engineering Students -- Appendix B List of Stop Words -- Bibliography -- Index. | |
| 520 | |a 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. | ||
| 588 | 0 | |a Print version record. | |
| 650 | 0 | |a Data mining. |0 http://id.loc.gov/authorities/subjects/sh97002073 | |
| 650 | 0 | |a Graph theory |x Data processing. |0 http://id.loc.gov/authorities/subjects/sh85056472 | |
| 650 | 0 | |a Algorithms. |0 http://id.loc.gov/authorities/subjects/sh85003487 | |
| 650 | 0 | |a Multidimensional scaling. |0 http://id.loc.gov/authorities/subjects/sh85088344 | |
| 650 | 7 | |a COMPUTERS |x Database Management |x Data Mining. |2 bisacsh | |
| 650 | 7 | |a Algorithms. |2 fast |0 (OCoLC)fst00805020 | |
| 650 | 7 | |a Data mining. |2 fast |0 (OCoLC)fst00887946 | |
| 650 | 7 | |a Graph theory |x Data processing. |2 fast |0 (OCoLC)fst00946587 | |
| 650 | 7 | |a Multidimensional scaling. |2 fast |0 (OCoLC)fst01028871 | |
| 655 | 4 | |a Electronic books. | |
| 655 | 0 | |a Electronic books. | |
| 700 | 1 | |a Schenker, Adam. | |
| 776 | 0 | 8 | |i Print version: |t Graph-theoretic techniques for web content mining. |d [Hackensack], N.J. ; London : World Scientific, ©2005 |z 9812563393 |w (DLC) 2006272427 |w (OCoLC)61424400 |
| 830 | 0 | |a Series in machine perception and artificial intelligence ; |v v. 62. |0 http://id.loc.gov/authorities/names/n91107585 | |
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