Mining Text Data


Author: Charu C. Aggarwal,ChengXiang Zhai
Publisher: Springer Science & Business Media
ISBN: 1461432235
Category: Computers
Page: 524
View: 3404

Continue Reading →

Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned. Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on Text Embedded with Heterogeneous and Multimedia Data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases. Mining Text Data simplifies the content, so that advanced-level students, practitioners and researchers in computer science can benefit from this book. Academic and corporate libraries, as well as ACM, IEEE, and Management Science focused on information security, electronic commerce, databases, data mining, machine learning, and statistics are the primary buyers for this reference book.

Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications


Author: Gary Miner,John Elder IV,Andrew Fast,Thomas Hill,Robert Nisbet,Dursun Delen
Publisher: Academic Press
ISBN: 0123870119
Category: Mathematics
Page: 1000
View: 5414

Continue Reading →

Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. Winner of a 2012 PROSE Award in Computing and Information Sciences from the Association of American Publishers, this book presents a comprehensive how-to reference that shows the user how to conduct text mining and statistically analyze results. In addition to providing an in-depth examination of core text mining and link detection tools, methods and operations, the book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection using real world example tutorials in such varied fields as corporate, finance, business intelligence, genomics research, and counterterrorism activities. The world contains an unimaginably vast amount of digital information which is getting ever vaster ever more rapidly. This makes it possible to do many things that previously could not be done: spot business trends, prevent diseases, combat crime and so on. Managed well, the textual data can be used to unlock new sources of economic value, provide fresh insights into science and hold governments to account. As the Internet expands and our natural capacity to process the unstructured text that it contains diminishes, the value of text mining for information retrieval and search will increase dramatically. Extensive case studies, most in a tutorial format, allow the reader to 'click through' the example using a software program, thus learning to conduct text mining analyses in the most rapid manner of learning possible Numerous examples, tutorials, power points and datasets available via companion website on Elsevierdirect.com Glossary of text mining terms provided in the appendix

Multidimensional Mining of Massive Text Data


Author: Chao Zhang,Jiawei Han
Publisher: Morgan & Claypool Publishers
ISBN: 1681735202
Category: Computers
Page: 198
View: 9044

Continue Reading →

Unstructured text, as one of the most important data forms, plays a crucial role in data-driven decision making in domains ranging from social networking and information retrieval to scientific research and healthcare informatics. In many emerging applications, people's information need from text data is becoming multidimensional—they demand useful insights along multiple aspects from a text corpus. However, acquiring such multidimensional knowledge from massive text data remains a challenging task. This book presents data mining techniques that turn unstructured text data into multidimensional knowledge. We investigate two core questions. (1) How does one identify task-relevant text data with declarative queries in multiple dimensions? (2) How does one distill knowledge from text data in a multidimensional space? To address the above questions, we develop a text cube framework. First, we develop a cube construction module that organizes unstructured data into a cube structure, by discovering latent multidimensional and multi-granular structure from the unstructured text corpus and allocating documents into the structure. Second, we develop a cube exploitation module that models multiple dimensions in the cube space, thereby distilling from user-selected data multidimensional knowledge. Together, these two modules constitute an integrated pipeline: leveraging the cube structure, users can perform multidimensional, multigranular data selection with declarative queries; and with cube exploitation algorithms, users can extract multidimensional patterns from the selected data for decision making. The proposed framework has two distinctive advantages when turning text data into multidimensional knowledge: flexibility and label-efficiency. First, it enables acquiring multidimensional knowledge flexibly, as the cube structure allows users to easily identify task-relevant data along multiple dimensions at varied granularities and further distill multidimensional knowledge. Second, the algorithms for cube construction and exploitation require little supervision; this makes the framework appealing for many applications where labeled data are expensive to obtain.

Text Data Management and Analysis

A Practical Introduction to Information Retrieval and Text Mining
Author: ChengXiang Zhai,Sean Massung
Publisher: Morgan & Claypool
ISBN: 1970001186
Category: Computers
Page: 530
View: 1489

Continue Reading →

Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. This has led to an increasing demand for powerful software tools to help people analyze and manage vast amounts of text data effectively and efficiently. Unlike data generated by a computer system or sensors, text data are usually generated directly by humans, and are accompanied by semantically rich content. As such, text data are especially valuable for discovering knowledge about human opinions and preferences, in addition to many other kinds of knowledge that we encode in text. In contrast to structured data, which conform to well-defined schemas (thus are relatively easy for computers to handle), text has less explicit structure, requiring computer processing toward understanding of the content encoded in text. The current technology of natural language processing has not yet reached a point to enable a computer to precisely understand natural language text, but a wide range of statistical and heuristic approaches to analysis and management of text data have been developed over the past few decades. They are usually very robust and can be applied to analyze and manage text data in any natural language, and about any topic. This book provides a systematic introduction to all these approaches, with an emphasis on covering the most useful knowledge and skills required to build a variety of practically useful text information systems. The focus is on text mining applications that can help users analyze patterns in text data to extract and reveal useful knowledge. Information retrieval systems, including search engines and recommender systems, are also covered as supporting technology for text mining applications. The book covers the major concepts, techniques, and ideas in text data mining and information retrieval from a practical viewpoint, and includes many hands-on exercises designed with a companion software toolkit (i.e., MeTA) to help readers learn how to apply techniques of text mining and information retrieval to real-world text data and how to experiment with and improve some of the algorithms for interesting application tasks. The book can be used as a textbook for a computer science undergraduate course or a reference book for practitioners working on relevant problems in analyzing and managing text data.

Data Mining and Statistics for Decision Making


Author: Stéphane Tufféry
Publisher: John Wiley & Sons
ISBN: 9780470979280
Category: Computers
Page: 716
View: 7387

Continue Reading →

Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives. This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized linear models, regularized regression, PLS regression, decision trees, neural networks, support vector machines, Vapnik theory, naive Bayesian classifier, ensemble learning and detection of association rules. They are discussed along with illustrative examples throughout the book to explain the theory of these methods, as well as their strengths and limitations. Key Features: Presents a comprehensive introduction to all techniques used in data mining and statistical learning, from classical to latest techniques. Starts from basic principles up to advanced concepts. Includes many step-by-step examples with the main software (R, SAS, IBM SPSS) as well as a thorough discussion and comparison of those software. Gives practical tips for data mining implementation to solve real world problems. Looks at a range of tools and applications, such as association rules, web mining and text mining, with a special focus on credit scoring. Supported by an accompanying website hosting datasets and user analysis. Statisticians and business intelligence analysts, students as well as computer science, biology, marketing and financial risk professionals in both commercial and government organizations across all business and industry sectors will benefit from this book.

Principles of Data Mining and Knowledge Discovery

Third European Conference, PKDD'99 Prague, Czech Republic, September 15-18, 1999 Proceedings
Author: Jan Zytkow,Jan Rauch
Publisher: Springer Science & Business Media
ISBN: 3540664904
Category: Computers
Page: 593
View: 8894

Continue Reading →

This book constitutes the refereed proceedings of the Third European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD'99, held in Prague, Czech Republic in September 1999. The 28 revised full papers and 48 poster presentations were carefully reviewed and selected from 106 full papers submitted. The papers are organized in topical sections on time series, applications, taxonomies and partitions, logic methods, distributed and multirelational databases, text mining and feature selection, rules and induction, and interesting and unusual issues.

Pattern Detection and Discovery

ESF Exploratory Workshop, London, UK, September 16-19, 2002.
Author: David J. Hand,England) Esf Exploratory Workshop (2002 London,Niall M. Adams
Publisher: Springer Science & Business Media
ISBN: 3540441484
Category: Computers
Page: 226
View: 3771

Continue Reading →

This book constitutes the refereed proceedings of an international workshop on Pattern Detection and Discovery organized by the European Science Foundation in London, UK in September 2002. The 17 revised full papers presented were carefully selected and reviewed for inclusion in this state-of-the-art book. Six papers present an introduction and general issues in the emerging field. Four papers are devoted to association rules. Four papers deal with various aspects of text mining and Web mining, and three papers explore advanced applications.

Data Mining

The Textbook
Author: Charu C. Aggarwal
Publisher: Springer
ISBN: 3319141422
Category: Computers
Page: 734
View: 1630

Continue Reading →

This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories: Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems. Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data. Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor. Appropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances mathematical details and intuition. It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners (including those with a limited mathematical background). Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples. Praise for Data Mining: The Textbook - “As I read through this book, I have already decided to use it in my classes. This is a book written by an outstanding researcher who has made fundamental contributions to data mining, in a way that is both accessible and up to date. The book is complete with theory and practical use cases. It’s a must-have for students and professors alike!" -- Qiang Yang, Chair of Computer Science and Engineering at Hong Kong University of Science and Technology "This is the most amazing and comprehensive text book on data mining. It covers not only the fundamental problems, such as clustering, classification, outliers and frequent patterns, and different data types, including text, time series, sequences, spatial data and graphs, but also various applications, such as recommenders, Web, social network and privacy. It is a great book for graduate students and researchers as well as practitioners." -- Philip S. Yu, UIC Distinguished Professor and Wexler Chair in Information Technology at University of Illinois at Chicago

Investigative Data Mining for Security and Criminal Detection


Author: Jesus Mena
Publisher: Butterworth-Heinemann
ISBN: 9780750676137
Category: Computers
Page: 452
View: 5414

Continue Reading →

Data mining has traditionally been used to predict consumer behaviour, but in the wake of 9/11, the same tools and techniques can also be used to detect and validate the identity of threatening and criminal entities for security purposes.

Data Mining Algorithms

Explained Using R
Author: Pawel Cichosz
Publisher: John Wiley & Sons
ISBN: 1118950801
Category: Mathematics
Page: 720
View: 8659

Continue Reading →

Data Mining Algorithms is a practical, technically-oriented guide to data mining algorithms that covers the most important algorithms for building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality evaluation, and creating model ensembles. The author presents many of the important topics and methodologies widely used in data mining, whilst demonstrating the internal operation and usage of data mining algorithms using examples in R.

Medical Informatics

Knowledge Management and Data Mining in Biomedicine
Author: Hsinchun Chen,Sherrilynne S. Fuller,Carol Friedman,William Hersh
Publisher: Springer Science & Business Media
ISBN: 038725739X
Category: Medical
Page: 648
View: 5658

Continue Reading →

Comprehensively presents the foundations and leading application research in medical informatics/biomedicine. The concepts and techniques are illustrated with detailed case studies. Authors are widely recognized professors and researchers in Schools of Medicine and Information Systems from the University of Arizona, University of Washington, Columbia University, and Oregon Health & Science University. Related Springer title, Shortliffe: Medical Informatics, has sold over 8000 copies The title will be positioned at the upper division and graduate level Medical Informatics course and a reference work for practitioners in the field.

Data Mining


Author: Jürgen Cleve,Uwe Lämmel
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110456907
Category: Computers
Page: 328
View: 5715

Continue Reading →

In den riesigen Datenbergen moderner Datenbanken steckt unentdecktes Wissen, das ohne geeignete Hilfsmittel kaum zu Tage gefördert werden kann. Hier setzt das Data Mining an und liefert Methoden und Algorithmen, um bisher unbekannte Zusammenhänge zu entdecken. Nach der Vermittlung der Grundlagen und Anwendungsklassen des Data Mining in den ersten beiden Kapiteln wird in Kapitel 3 auf geeignete Darstellungsmöglichkeiten für Data-Mining-Modelle eingegangen; Kapitel 4 behandelt die Algorithmen und Verfahrensklassen, Kapitel 5 geht auf konkrete Anwendungsarchitekturen ein. Das Buch deckt den Stoff einer einsemestrigen Vorlesung zu Data Mining an Universitäten oder Fachhochschulen ab und ist als klassisches Lehrbuch konzipiert. Es bietet Zusammenfassungen, zahlreiche Beispiele und Übungsaufgaben.

Applied Insurance Analytics

A Framework for Driving More Value from Data Assets, Technologies, and Tools
Author: Patricia L Saporito
Publisher: FT Press
ISBN: 0133760731
Category: Computers
Page: 208
View: 1715

Continue Reading →

Insurers: use analytics to drive far more value from your most important asset -- data! Today, many insurers radically underutilize their data, leaving them vulnerable to traditional and non-traditional competitors alike. Now, drawing on 25 years of industry experience, Patricia Saporito shows how to systematically leverage analytics to improve business performance and customer satisfaction throughout any insurance business. Applied Insurance Analytics demonstrates how to use analytics to systematically improve operations ranging from underwriting and risk management to claims. Even more important: it will help you drive more value everywhere by defining a focused enterprise-wide analytics strategy, and overcoming the challenges that stand in your way. Saporito helps you assess your current analytics maturity, choose the new applications that offer the most value, and master best practices from throughout the industry and beyond. Throughout, she helps you gain more value from data assets, technologies and tools you've already invested in. You'll find new case studies, practical tools, and easy templates for improving the "Analytics IQ" of your entire enterprise. For every insurance industry professional and manager concerned with analytics, including users, IT pros, sales/marketing specialists, and data scientists. This book will also be valuable to students in any MBA or other program focused on insurance or risk management, and to many students in IT or analytics-specific programs.

Managing and Mining Multimedia Databases


Author: Bhavani Thuraisingham
Publisher: CRC Press
ISBN: 1420042556
Category: Computers
Page: 352
View: 5594

Continue Reading →

There is now so much data on the Web that managing it with conventional tools is becoming almost impossible. To manage this data, provide interoperability and warehousing between multiple data sources and systems, and extract information from the databases and warehouses, various tools are being developed. In fact, developments in multimedia database management have exploded during the past decade. To date, however, there has been little information available on providing a complete set of services for multimedia databases, including their management, mining, and integration on the Web for electronic enterprises. Managing and Mining Multimedia Databases fills that gap. Focusing on managing and mining multimedia databases for electronic commerce and business, it explores database management system techniques for text, image, audio, and video databases. It addresses the issues and challenges of mining multimedia databases to extract information, and discusses the directions and challenges related to integrating multimedia databases for the Web, particularly for e-business. This book provides a comprehensive overview of multimedia data management and mining technologies, from the underlying concepts, architectures, and data models for multimedia database systems to the technologies that support multimedia data management on the Web, privacy issues, and emerging standards, prototypes, and products. Designed for technical managers, executives, and technologists, it offers your only opportunity to learn about both multimedia data management and multimedia data mining within a single book.

Text Mining

Predictive Methods for Analyzing Unstructured Information
Author: Sholom M. Weiss,Nitin Indurkhya,Tong Zhang,Fred Damerau
Publisher: Springer Science & Business Media
ISBN: 9780387345550
Category: Computers
Page: 237
View: 6425

Continue Reading →

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.

Fundamentals of Predictive Text Mining


Author: Sholom M. Weiss,Nitin Indurkhya,Tong Zhang
Publisher: Springer Science & Business Media
ISBN: 9781849962261
Category: Computers
Page: 226
View: 8440

Continue Reading →

One consequence of the pervasive use of computers is that most documents originate in digital form. Widespread use of the Internet makes them readily available. Text mining – the process of analyzing unstructured natural-language text – is concerned with how to extract information from these documents. Developed from the authors’ highly successful Springer reference on text mining, Fundamentals of Predictive Text Mining is an introductory textbook and guide to this rapidly evolving field. Integrating topics spanning the varied disciplines of data mining, machine learning, databases, and computational linguistics, this uniquely useful book also provides practical advice for text mining. In-depth discussions are presented on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Background on data mining is beneficial, but not essential. Where advanced concepts are discussed that require mathematical maturity for a proper understanding, intuitive explanations are also provided for less advanced readers. Topics and features: presents a comprehensive, practical and easy-to-read introduction to text mining; includes chapter summaries, useful historical and bibliographic remarks, and classroom-tested exercises for each chapter; explores the application and utility of each method, as well as the optimum techniques for specific scenarios; provides several descriptive case studies that take readers from problem description to systems deployment in the real world; includes access to industrial-strength text-mining software that runs on any computer; describes methods that rely on basic statistical techniques, thus allowing for relevance to all languages (not just English); contains links to free downloadable software and other supplementary instruction material. Fundamentals of Predictive Text Mining is an essential resource for IT professionals and managers, as well as a key text for advanced undergraduate computer science students and beginning graduate students. Dr. Sholom M. Weiss is a Research Staff Member with the IBM Predictive Modeling group, in Yorktown Heights, New York, and Professor Emeritus of Computer Science at Rutgers University. Dr. Nitin Indurkhya is Professor at the School of Computer Science and Engineering, University of New South Wales, Australia, as well as founder and president of data-mining consulting company Data-Miner Pty Ltd. Dr. Tong Zhang is Associate Professor at the Department of Statistics and Biostatistics at Rutgers University, New Jersey.

Learning Data Mining with R


Author: Bater Makhabel
Publisher: Packt Publishing Ltd
ISBN: 178398211X
Category: Computers
Page: 314
View: 9669

Continue Reading →

This book is intended for the budding data scientist or quantitative analyst with only a basic exposure to R and statistics. This book assumes familiarity with only the very basics of R, such as the main data types, simple functions, and how to move data around. No prior experience with data mining packages is necessary; however, you should have a basic understanding of data mining concepts and processes.

Text Mining - Going Way Beyond Just Listening to the Voice of the Customer


Author: Forte Consultancy Group
Publisher: Forte Consultancy
ISBN: N.A
Category:
Page: N.A
View: 3600

Continue Reading →

How about making use of the 80% of customer data you have on hand but haven’t tapped into yet? And how about if that data can help you reduce churn by 50%? Text mining is one of the latest trends in data mining today, with many companies already benefiting significantly from their efforts around this practice.