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Graph Theoretic Techniques For Web Content Mining
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Book Synopsis Graph-theoretic Techniques for Web Content Mining by : Adam Schenker
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 250 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.
Book Synopsis Graph-theoretic Techniques for Web Content Mining by : Adam Schenker
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.
Book Synopsis Mining Graph Data by : Diane J. Cook
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.
Book Synopsis Smart Computing by : Mohammad Ayoub Khan
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.
Book Synopsis Graph Mining by : Deepayan Chakrabarti
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
Book Synopsis Graph-theoretic Techniques for Web Content Mining by :
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:
Book Synopsis Graph-theoretic Techniques For Web Content Mining by : Adam Schenker
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 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.
Book Synopsis Practical Graph Mining with R by : Nagiza F. Samatova
Download or read book Practical Graph Mining with R written by Nagiza F. Samatova and published by CRC Press. This book was released on 2013-07-15 with total page 495 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover Novel and Insightful Knowledge from Data Represented as a GraphPractical Graph Mining with R presents a "do-it-yourself" approach to extracting interesting patterns from graph data. It covers many basic and advanced techniques for the identification of anomalous or frequently recurring patterns in a graph, the discovery of groups or cluste
Book Synopsis Data Mining the Web by : Zdravko Markov
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).
Book Synopsis Handbook Of Pattern Recognition And Computer Vision (2nd Edition) by : Chi Hau Chen
Download or read book Handbook Of Pattern Recognition And Computer Vision (2nd Edition) written by Chi Hau Chen and published by World Scientific. This book was released on 1999-03-12 with total page 1045 pages. Available in PDF, EPUB and Kindle. Book excerpt: The very significant advances in computer vision and pattern recognition and their applications in the last few years reflect the strong and growing interest in the field as well as the many opportunities and challenges it offers. The second edition of this handbook represents both the latest progress and updated knowledge in this dynamic field. The applications and technological issues are particularly emphasized in this edition to reflect the wide applicability of the field in many practical problems. To keep the book in a single volume, it is not possible to retain all chapters of the first edition. However, the chapters of both editions are well written for permanent reference. This indispensable handbook will continue to serve as an authoritative and comprehensive guide in the field.
Download or read book Web Data Mining written by Bing Liu and published by Springer Science & Business Media. This book was released on 2011-06-25 with total page 637 pages. Available in PDF, EPUB and Kindle. Book excerpt: Liu has written a comprehensive text on Web mining, which consists of two parts. The first part covers the data mining and machine learning foundations, where all the essential concepts and algorithms of data mining and machine learning are presented. The second part covers the key topics of Web mining, where Web crawling, search, social network analysis, structured data extraction, information integration, opinion mining and sentiment analysis, Web usage mining, query log mining, computational advertising, and recommender systems are all treated both in breadth and in depth. His book thus brings all the related concepts and algorithms together to form an authoritative and coherent text. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in Web mining and data mining both as a learning text and as a reference book. Professors can readily use it for classes on data mining, Web mining, and text mining. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.
Book Synopsis Wavelet Theory Approach to Pattern Recognition by : Yuan Yan Tang
Download or read book Wavelet Theory Approach to Pattern Recognition written by Yuan Yan Tang and published by World Scientific Publishing Company. This book was released on 2009 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ch. 1. Introduction. 1.1. Wavelet : a novel mathematical tool for pattern recognition. 1.2. Brief review of pattern recognition with wavelet theory -- ch. 2. Continuous wavelet transforms. 2.1. General theory of continuous wavelet transforms. 2.2. The continuous wavelet transform as a filter. 2.3. Characterization of Lipschitz regularity of signal by wavelet. 2.4. Some examples of basic wavelets -- ch. 3. Multiresolution analysis and wavelet bases. 3.1. Multiresolution analysis. 3.2. The construction of MRAs. 3.3. The construction of biorthonormal wavelet bases. 3.4. S. mallat algorithms -- ch. 4. Some typical wavelet bases. 4.1. Orthonormal wavelet bases. 4.2. Nonorthonormal wavelet bases -- ch. 5. Step-edge detection by wavelet transform. 5.1. Edge detection with local maximal modulus of wavelet transform. 5.2. Calculation of W[symbol]f(x) and W[symbol]f(x, y). 5.3. Wavelet transform for contour extraction and background removal -- ch. 6. Characterization of dirac-edges with quadratic spline wavelet transform. 6.1. Selection of wavelet functions by derivation. 6.2. Characterization of dirac-structure edges by wavelet transform. 6.3. Experiments -- ch. 7. Construction of new wavelet function and application to curve analysis. 7.1. Construction of new wavelet function - Tang-Yang wavelet. 7.2. Characterization of curves through new wavelet transform. 7.3. Comparison with other wavelets. 7.4. Algorithm and experiments -- ch. 8. Skeletonization of ribbon-like shapes with new wavelet function. 8.1. Tang-Yang wavelet function. 8.2. Characterization of the boundary of a shape by wavelet transform. 8.3. Wavelet skeletons and its implementation. 8.4. Algorithm and experiment -- ch. 9. Feature extraction by wavelet sub-patterns and divider dimensions. 9.1. Dimensionality reduction of two-dimensional patterns with ring-projection. 9.2. Wavelet orthonormal decomposition to produce sub-patterns. 9.3. Wavelet-fractal scheme. 9.4. Experiments -- ch. 10. Document analysis by reference line detection with 2-D wavelet transform. 10.1. Two-dimensional MRA and mallat algorithm. 10.2. Detection of reference line from sub-images by the MRA. 10.3. Experiments -- ch. 11. Chinese character processing with B-spline wavelet transform. 11.1. Compression of Chinese character. 11.2. Enlargement of type size with arbitrary scale based on wavelet transform. 11.3. Generation of Chinese type style based on wavelet transform -- ch. 12. Classifier design based on orthogonal wavelet series. 12.1. Fundamentals. 12.2. Minimum average lose classifier design. 12.3. Minimum error-probability classifier design. 12.4. Probability density estimation based on orthogonal wavelet series
Book Synopsis Scalable Algorithms for Data and Network Analysis by : Shang-Hua Teng
Download or read book Scalable Algorithms for Data and Network Analysis written by Shang-Hua Teng and published by . This book was released on 2016-05-04 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the age of Big Data, efficient algorithms are in high demand. It is also essential that efficient algorithms should be scalable. This book surveys a family of algorithmic techniques for the design of scalable algorithms. These techniques include local network exploration, advanced sampling, sparsification, and geometric partitioning.
Book Synopsis Managing and Mining Graph Data by : Charu C. Aggarwal
Download or read book Managing and Mining Graph Data written by Charu C. Aggarwal and published by Springer Science & Business Media. This book was released on 2010-02-02 with total page 623 pages. Available in PDF, EPUB and Kindle. Book excerpt: Managing and Mining Graph Data is a comprehensive survey book in graph management and mining. It contains extensive surveys on a variety of important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by well known researchers in the field, and provide a broad perspective of the area. This is the first comprehensive survey book in the emerging topic of graph data processing. Managing and Mining Graph Data is designed for a varied audience composed of professors, researchers and practitioners in industry. This volume is also suitable as a reference book for advanced-level database students in computer science and engineering.
Download or read book Data Mining written by Hillol Kargupta and published by . This book was released on 2004 with total page 582 pages. Available in PDF, EPUB and Kindle. Book excerpt: A state-of-the-art survey of recent advances in data mining or knowledge discovery.
Download or read book Web Mining written by Anthony Scime and published by IGI Global. This book was released on 2005-01-01 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Web Mining is moving the World Wide Web toward a more useful environment in which users can quickly and easily find the information they need. Web Mining uses document content, hyperlink structure, and usage statistics to assist users in meeting their needed information. This book provides a record of current research and practical applications in Web searching. It includes techniques that will improve the utilization of the Web by the design of Web sites, as well as the design and application of search agents. This book presents research and related applications in a manner that encourages additional work toward improving the reduction of information overflow, which is so common today in Web search results.
Book Synopsis Mining of Massive Datasets by : Jure Leskovec
Download or read book Mining of Massive Datasets written by Jure Leskovec and published by Cambridge University Press. This book was released on 2014-11-13 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.
