Technological advances are revolutionizing computers and networks to support digital video and audio, leading to new design spaces in computer systems and applications. Under the surface of exciting multimedia technologies lies a mine of research problems. This volume presents the proceedings of an international workshop which brought together the leading researchers in all aspects of multimedia computing, communication, storage, and applications. The field of multimedia has witnessed an explosive growth in the last few years and the selection of papers for this workshop was extremely competitive. The volume contains 26 full papers and 14 short papers selected from 128 contributions, organized into parts on: network and operating system support for multimedia; multimedia on-demand services; media synchronization; distributed multimedia systems; network and operating system support for multimedia; multimedia models, frameworks, and document architectures; and multimedia workstations and platforms.

Text Mining: Predictive Methods for Analyzing Unstructured Information (Lecture Notes in Computer Science)

Computers & Technology

Weiss, Sholom M.

Indurkhya, Nitin

Zhang, Tong

Certification

Springer

Data mining is a mature technology. The prediction problem, looking for predictive patterns in data, has been widely studied. Strong me- ods are available to the practitioner. These methods process structured numerical information, where uniform measurements are taken over a sample of data. Text is often described as unstructured information. So, it would seem, text and numerical data are different, requiring different methods. Or are they? In our view, a prediction problem can be solved by the same methods, whether the data are structured - merical measurements or unstructured text. Text and documents can be transformed into measured values, such as the presence or absence of words, and the same methods that have proven successful for pred- tive data mining can be applied to text. Yet, there are key differences. Evaluation techniques must be adapted to the chronological order of publication and to alternative measures of error. Because the data are documents, more specialized analytical methods may be preferred for text. Moreover, the methods must be modi?ed to accommodate very high dimensions: tens of thousands of words and documents. Still, the central themes are similar.

Text Mining: Predictive Methods for Analyzing Unstructured Information

Damerau, Fred

Computer Science

Data mining is a mature technology. The prediction problem, looking for predictive patterns in data, has been widely studied. Strong me- ods are available to the practitioner. These methods process structured numerical information, where uniform measurements are taken over a sample of data. Text is often described as unstructured information. So, it would seem, text and numerical data are different, requiring different methods. Or are they? In our view, a prediction problem can be solved by the same methods, whether the data are structured - merical measurements or unstructured text. Text and documents can be transformed into measured values, such as the presence or absence of words, and the same methods that have proven successful for pred- tive data mining can be applied to text. Yet, there are key differences. Evaluation techniques must be adapted to the chronological order of publication and to alternative measures of error. Because the data are documents, more specialized analytical methods may be preferred for text. Moreover, the methods must be modi?ed to accommodate very high dimensions: tens of thousands of words and documents. Still, the central themes are similar.

Weiss, Sholom M. M.

Fundamentals of Predictive Text Mining (Texts in Computer Science)

This is an introductory textbook and guide to the rapidly evolving field of predictive text mining. There are chapter summaries, historical and bibliographic remarks, and classroom-tested exercises for each chapter. Descriptive case studies are also included.

This successful textbook on predictive text mining offers a unified perspective on a rapidly evolving field, integrating topics spanning the varied disciplines of data science, machine learning, databases, and computational linguistics. Serving also as a practical guide, this unique book provides helpful advice illustrated by examples and case studies. This highly anticipated second edition has been thoroughly revised and expanded with new material on deep learning, graph models, mining social media, errors and pitfalls in big data evaluation, Twitter sentiment analysis, and dependency parsing discussion. The fully updated content also features in-depth discussions on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Features: includes chapter summaries and exercises; explores the application of each method; provides several case studies; contains links to free text-mining software.

The potential business advantages of data mining are well documented in publications for executives and managers. However, developers implementing major data-mining systems need concrete information about the underlying technical principles―and their practical manifestations―in order to either integrate commercially available tools or write data-mining programs from scratch. This book is the first technical guide to provide a complete, generalized roadmap for developing data-mining applications, together with advice on performing these large-scale, open-ended analyses for real-world data warehouses. Note: If you already own Predictive Data Mining: A Practical Guide, please see ISBN 1-55860-477-4 to order the accompanying software. To order the book/software package, please see ISBN 1-55860-478-2.

Predictive Data Mining: A Practical Guide (The Morgan Kaufmann Series in Data Management Systems)

Morgan Kaufmann

The potential business advantages of data mining are well documented in publications for executives and managers. However, developers implementing major data-mining systems need concrete information about the underlying technical principles and their practical manifestations in order to either integrate commercially available tools or write data-mining programs from scratch. This book is the first technical guide to provide a complete, generalized roadmap for developing data-mining applications, together with advice on performing these large-scale, open-ended analyses for real-world data warehouses. 
A state-of-the-art data-mining software kit accompanies the book. The software, which is delivered through a special web site, is a collection of routines for efficient mining of big data. Both classical and the more computationally expensive state-of-the-art prediction methods are included. Using a standard spreadsheet data format, this kit implements all of the data-mining tasks described in the book. The software is available for Windows 95/NT and Unix platforms (no need to specify when ordering). * Focuses on the preparation and organization of data and the development of an overall strategy for data mining. * Reviews sophisticated prediction methods that search for patterns in big data. * Describes how to accurately estimate future performance of proposed solutions. * Illustrates the data-mining process and its potential pitfalls through real-life case studies.

Predictive Data Mining: A Practical Guide (with Software)

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Books by Indurkhya, Nitin

See the best price to sell, buy, or rent books by Indurkhya, Nitin. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Text Mining: Predictive Methods for Analyzing Unstructured Information (Lecture Notes in Computer Science)

Text Mining: Predictive Methods for Analyzing Unstructured Information

Text Mining: Predictive Methods for Analyzing Unstructured Information

Fundamentals of Predictive Text Mining (Texts in Computer Science)

Fundamentals of Predictive Text Mining (Texts in Computer Science)

Predictive Data Mining: A Practical Guide (The Morgan Kaufmann Series in Data Management Systems)

Predictive Data Mining: A Practical Guide (with Software)