Managing Gigabytes

Autor: Ian H. Witten
Publisher: Morgan Kaufmann
ISBN: 9781558605701
File Size: 31,60 MB
Format: PDF, ePub
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In this fully updated second edition of the highly acclaimed Managing Gigabytes, authors Witten, Moffat, and Bell continue to provide unparalleled coverage of state-of-the-art techniques for compressing and indexing data. Whatever your field, if you work with large quantities of information, this book is essential reading--an authoritative theoretical resource and a practical guide to meeting the toughest storage and access challenges. It covers the latest developments in compression and indexing and their application on the Web and in digital libraries. It also details dozens of powerful techniques supported by mg, the authors' own system for compressing, storing, and retrieving text, images, and textual images. mg's source code is freely available on the Web. * Up-to-date coverage of new text compression algorithms such as block sorting, approximate arithmetic coding, and fat Huffman coding * New sections on content-based index compression and distributed querying, with 2 new data structures for fast indexing * New coverage of image coding, including descriptions of de facto standards in use on the Web (GIF and PNG), information on CALIC, the new proposed JPEG Lossless standard, and JBIG2 * New information on the Internet and WWW, digital libraries, web search engines, and agent-based retrieval * Accompanied by a public domain system called MG which is a fully worked-out operational example of the advanced techniques developed and explained in the book * New appendix on an existing digital library system that uses the MG software

Managing Gigabytes

Autor: Ian H. Witten
Publisher: Kluwer Academic Publishers
ISBN: 9780442018634
File Size: 67,28 MB
Format: PDF
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Overview; Text compression; Indexing; querying; Index construction; Image compression; Textual images; Mixed text and images; Implementation; The information explosion; Guide to the mg system; References; Index.

Data Mining Practical Machine Learning Tools And Techniques

Autor: Ian H. Witten
Publisher: Elsevier
ISBN: 0080890369
File Size: 12,67 MB
Format: PDF, ePub, Mobi
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Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

How To Build A Digital Library

Autor: Ian H. Witten
Publisher: Morgan Kaufmann
ISBN: 9780080890395
File Size: 17,89 MB
Format: PDF, ePub, Mobi
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How to Build a Digital Library reviews knowledge and tools to construct and maintain a digital library, regardless of the size or purpose. A resource for individuals, agencies, and institutions wishing to put this powerful tool to work in their burgeoning information treasuries. The Second Edition reflects developments in the field as well as in the Greenstone Digital Library open source software. In Part I, the authors have added an entire new chapter on user groups, user support, collaborative browsing, user contributions, and so on. There is also new material on content-based queries, map-based queries, cross-media queries. There is an increased emphasis placed on multimedia by adding a "digitizing" section to each major media type. A new chapter has also been added on "internationalization," which will address Unicode standards, multi-language interfaces and collections, and issues with non-European languages (Chinese, Hindi, etc.). Part II, the software tools section, has been completely rewritten to reflect the new developments in Greenstone Digital Library Software, an internationally popular open source software tool with a comprehensive graphical facility for creating and maintaining digital libraries. Outlines the history of libraries on both traditional and digital Written for both technical and non-technical audiences and covers the entire spectrum of media, including text, images, audio, video, and related XML standards Web-enhanced with software documentation, color illustrations, full-text index, source code, and more

Web Dragons

Autor: Ian H. Witten
Publisher: Elsevier
ISBN: 0080469094
File Size: 49,57 MB
Format: PDF, ePub
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Web Dragons offers a perspective on the world of Web search and the effects of search engines and information availability on the present and future world. In the blink of an eye since the turn of the millennium, the lives of people who work with information have been utterly transformed. Everything we need to know is on the web. It's where we learn and play, shop and do business, keep up with old friends and meet new ones. Search engines make it possible for us to find the stuff we need to know. Search engines — web dragons — are the portals through which we access society's treasure trove of information. How do they stack up against librarians, the gatekeepers over centuries past? What role will libraries play in a world whose information is ruled by the web? How is the web organized? Who controls its contents, and how do they do it? How do search engines work? How can web visibility be exploited by those who want to sell us their wares? What's coming tomorrow, and can we influence it? As we witness the dawn of a new era, this book shows readers what it will look like and how it will change their world. Whoever you are: if you care about information, this book will open your eyes and make you blink. Presents a critical view of the idea of funneling information access through a small handful of gateways and the notion of a centralized index--and the problems that may cause Provides promising approaches for addressing the problems, such as the personalization of web services Presented by authorities in the field of digital libraries, web history, machine learning, and web and data mining Find more information at the author's site: webdragons.net

Introduction To Information Retrieval

Autor: Christopher D. Manning
Publisher: Cambridge University Press
ISBN: 1139472100
File Size: 30,71 MB
Format: PDF, ePub, Mobi
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Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.

Compression And Coding Algorithms

Autor: Alistair Moffat
Publisher: Springer Science & Business Media
ISBN: 9780792376682
File Size: 36,92 MB
Format: PDF
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An authoritative reference to the whole area of source coding algorithms, Compression and Coding Algorithms will be a primary resource for both researchers and software engineers. The book also will be interest for people in broader area of design and analysis of algorithms and data structure. Practitioners, especially those who work in the software development and independent consulting industries creating compression software or other applications systems, in which compression plays a part, will benefit from techniques that are described. Compression and Coding Algorithms describes in detail the coding mechanisms that are available for use in data compression systems. The well known Huffman coding technique is one mechanism, but there have been many others developed over the past few decades, and this book describes, explains and assesses them. People undertaking research of software development in the areas of compression and coding algorithms will find this book an indispensable reference. In particular, the careful and detailed description of algorithms and their implementation, plus accompanying pseudo-code that can be readily implemented on computer, make this book a definitive reference in an area currently without one. The detailed pseudo-code presentation of over thirty algorithms, and careful explanation of examples, make this book approachable and authoritative. Compression and throughput results are presented where appropriate, and serve as a validation of the assessments and recommendation made in the text. The combination of implementation detail, thoughtful discussions, and careful presentation means that this book will occupy a pivotal role in this area for many years. In-depth coverage of the crucial areas of minimum-redundancy coding, arithmetic coding, adaptive coding make Compression and Coding Algorithms unique in its field.

Data Mining Know It All

Autor: Soumen Chakrabarti
Publisher: Morgan Kaufmann
ISBN: 9780080877884
File Size: 21,73 MB
Format: PDF
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This book brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases. It consolidates both introductory and advanced topics, thereby covering the gamut of data mining and machine learning tactics ? from data integration and pre-processing, to fundamental algorithms, to optimization techniques and web mining methodology. The proposed book expertly combines the finest data mining material from the Morgan Kaufmann portfolio. Individual chapters are derived from a select group of MK books authored by the best and brightest in the field. These chapters are combined into one comprehensive volume in a way that allows it to be used as a reference work for those interested in new and developing aspects of data mining. This book represents a quick and efficient way to unite valuable content from leading data mining experts, thereby creating a definitive, one-stop-shopping opportunity for customers to receive the information they would otherwise need to round up from separate sources. Chapters contributed by various recognized experts in the field let the reader remain up to date and fully informed from multiple viewpoints. Presents multiple methods of analysis and algorithmic problem-solving techniques, enhancing the reader’s technical expertise and ability to implement practical solutions. Coverage of both theory and practice brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases.

Understanding Search Engines

Autor: Michael W. Berry
Publisher: SIAM
ISBN: 9780898718164
File Size: 33,11 MB
Format: PDF, Mobi
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The second edition of Understanding Search Engines: Mathematical Modeling and Text Retrieval follows the basic premise of the first edition by discussing many of the key design issues for building search engines and emphasizing the important role that applied mathematics can play in improving information retrieval. The authors discuss important data structures, algorithms, and software as well as user-centered issues such as interfaces, manual indexing, and document preparation. Readers will find that the second edition includes significant changes that bring the text up to date on current information retrieval methods. For example, the authors have added a completely new chapter on link-structure algorithms used in search engines such as Google, and the chapter on user interface has been rewritten to specifically focus on search engine usability. To reflect updates in the literature on information retrieval, the authors have added new recommendations for further reading and expanded the bibliography. In addition, the index has been updated and streamlined to make it more reader friendly.