Graph Theoretic Techniques For Web Content Mining

Author : Adam Schenker
Publisher : World Scientific
ISBN 13 : 9812563393
Total Pages : 249 pages
Book Rating : 4.92/5 ( download)

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Download or read book Graph-theoretic Techniques for Web Content Mining written by Adam Schenker and published by World Scientific. This book was released on 2005 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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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ISBN 13 :
Total Pages : pages
Book Rating : 4.74/5 ( download)

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Download or read book Graph-theoretic Techniques for Web Content Mining written by and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Author : Adam Schenker
Publisher : World Scientific
ISBN 13 : 9814480347
Total Pages : 248 pages
Book Rating : 4.45/5 ( download)

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Download or read book Graph-Theoretic Techniques for Web Content Mining written by Adam Schenker and published by World Scientific. This book was released on 2005-05-31 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. Contents:Introduction to Web MiningGraph Similarity TechniquesGraph Models for Web DocumentsGraph-Based ClusteringGraph-Based ClassificationThe Graph Hierarchy Construction Algorithm for Web Search Clustering Readership: Researchers and graduate students who are interested in computer science, specifically machine learning. Also of interest to researchers in academia or industry in disciplines such as information science or information technology who are interested in text and web documents. Keywords:Graph;Machine Learning;Web Mining;Data Mining;Clustering;Classification;Graph Distance;Maximum Common SubgraphKey Features:Opens up exciting new possibilities for utilizing graphs in common machine learning algorithmsPresents experimental results comparing differing graph representations and graph distance measuresProvides a review of graph-theoretic similarity techniques

Author : Diane J. Cook
Publisher : John Wiley & Sons
ISBN 13 : 0470073039
Total Pages : 501 pages
Book Rating : 4.32/5 ( download)

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Download or read book Mining Graph Data written by Diane J. Cook and published by John Wiley & Sons. This book was released on 2006-12-18 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text takes a focused and comprehensive look at mining data represented as a graph, with the latest findings and applications in both theory and practice provided. Even if you have minimal background in analyzing graph data, with this book you’ll be able to represent data as graphs, extract patterns and concepts from the data, and apply the methodologies presented in the text to real datasets. There is a misprint with the link to the accompanying Web page for this book. For those readers who would like to experiment with the techniques found in this book or test their own ideas on graph data, the Web page for the book should be http://www.eecs.wsu.edu/MGD.

Author : Qi Xuan
Publisher : Springer Nature
ISBN 13 : 981162609X
Total Pages : 256 pages
Book Rating : 4.98/5 ( download)

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Download or read book Graph Data Mining written by Qi Xuan and published by Springer Nature. This book was released on 2021-07-15 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph data is powerful, thanks to its ability to model arbitrary relationship between objects and is encountered in a range of real-world applications in fields such as bioinformatics, traffic network, scientific collaboration, world wide web and social networks. Graph data mining is used to discover useful information and knowledge from graph data. The complications of nodes, links and the semi-structure form present challenges in terms of the computation tasks, e.g., node classification, link prediction, and graph classification. In this context, various advanced techniques, including graph embedding and graph neural networks, have recently been proposed to improve the performance of graph data mining. This book provides a state-of-the-art review of graph data mining methods. It addresses a current hot topic – the security of graph data mining – and proposes a series of detection methods to identify adversarial samples in graph data. In addition, it introduces readers to graph augmentation and subgraph networks to further enhance the models, i.e., improve their accuracy and robustness. Lastly, the book describes the applications of these advanced techniques in various scenarios, such as traffic networks, social and technical networks, and blockchains.

Author : Mohammad Ayoub Khan
Publisher : CRC Press
ISBN 13 : 1000382613
Total Pages : 1110 pages
Book Rating : 4.17/5 ( download)

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Download or read book Smart Computing written by Mohammad Ayoub Khan and published by CRC Press. This book was released on 2021-05-12 with total page 1110 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of SMART technologies is an interdependent discipline. It involves the latest burning issues ranging from machine learning, cloud computing, optimisations, modelling techniques, Internet of Things, data analytics, and Smart Grids among others, that are all new fields. It is an applied and multi-disciplinary subject with a focus on Specific, Measurable, Achievable, Realistic & Timely system operations combined with Machine intelligence & Real-Time computing. It is not possible for any one person to comprehensively cover all aspects relevant to SMART Computing in a limited-extent work. Therefore, these conference proceedings address various issues through the deliberations by distinguished Professors and researchers. The SMARTCOM 2020 proceedings contain tracks dedicated to different areas of smart technologies such as Smart System and Future Internet, Machine Intelligence and Data Science, Real-Time and VLSI Systems, Communication and Automation Systems. The proceedings can be used as an advanced reference for research and for courses in smart technologies taught at graduate level.

Author : Françoise Fogelman-Soulié
Publisher : IOS Press
ISBN 13 : 1586038982
Total Pages : 388 pages
Book Rating : 4.84/5 ( download)

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Download or read book Mining Massive Data Sets for Security written by Françoise Fogelman-Soulié and published by IOS Press. This book was released on 2008 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: The real power for security applications will come from the synergy of academic and commercial research focusing on the specific issue of security. This book is suitable for those interested in understanding the techniques for handling very large data sets and how to apply them in conjunction for solving security issues.

Author : Deepayan Chakrabarti
Publisher : Morgan & Claypool Publishers
ISBN 13 : 160845116X
Total Pages : 209 pages
Book Rating : 4.66/5 ( download)

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Download or read book Graph Mining written by Deepayan Chakrabarti and published by Morgan & Claypool Publishers. This book was released on 2012-10-01 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: What does the Web look like? How can we find patterns, communities, outliers, in a social network? Which are the most central nodes in a network? These are the questions that motivate this work. Networks and graphs appear in many diverse settings, for example in social networks, computer-communication networks (intrusion detection, traffic management), protein-protein interaction networks in biology, document-text bipartite graphs in text retrieval, person-account graphs in financial fraud detection, and others. In this work, first we list several surprising patterns that real graphs tend to follow. Then we give a detailed list of generators that try to mirror these patterns. Generators are important, because they can help with "what if" scenarios, extrapolations, and anonymization. Then we provide a list of powerful tools for graph analysis, and specifically spectral methods (Singular Value Decomposition (SVD)), tensors, and case studies like the famous "pageRank" algorithm and the "HITS" algorithm for ranking web search results. Finally, we conclude with a survey of tools and observations from related fields like sociology, which provide complementary viewpoints. Table of Contents: Introduction / Patterns in Static Graphs / Patterns in Evolving Graphs / Patterns in Weighted Graphs / Discussion: The Structure of Specific Graphs / Discussion: Power Laws and Deviations / Summary of Patterns / Graph Generators / Preferential Attachment and Variants / Incorporating Geographical Information / The RMat / Graph Generation by Kronecker Multiplication / Summary and Practitioner's Guide / SVD, Random Walks, and Tensors / Tensors / Community Detection / Influence/Virus Propagation and Immunization / Case Studies / Social Networks / Other Related Work / Conclusions

Author : Simeon Simoff
Publisher : Springer Science & Business Media
ISBN 13 : 3540710795
Total Pages : 417 pages
Book Rating : 4.90/5 ( download)

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Download or read book Visual Data Mining written by Simeon Simoff and published by Springer Science & Business Media. This book was released on 2008-07-18 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: The importance of visual data mining, as a strong sub-discipline of data mining, had already been recognized in the beginning of the decade. In 2005 a panel of renowned individuals met to address the shortcomings and drawbacks of the current state of visual information processing. The need for a systematic and methodological development of visual analytics was detected. This book aims at addressing this need. Through a collection of 21 contributions selected from more than 46 submissions, it offers a systematic presentation of the state of the art in the field. The volume is structured in three parts on theory and methodologies, techniques, and tools and applications.

Author : Zdravko Markov
Publisher : John Wiley & Sons
ISBN 13 : 0470108088
Total Pages : 236 pages
Book Rating : 4.86/5 ( download)

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Download or read book Data Mining the Web written by Zdravko Markov and published by John Wiley & Sons. This book was released on 2007-04-06 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces the reader to methods of data mining on the web, including uncovering patterns in web content (classification, clustering, language processing), structure (graphs, hubs, metrics), and usage (modeling, sequence analysis, performance).