Practical Text Mining with Perl

Author: Roger Bilisoly

Publisher: John Wiley & Sons

ISBN: 1118210506

Category: Computers

Page: 296

View: 562

Provides readers with the methods, algorithms, and means to perform text mining tasks This book is devoted to the fundamentals of text mining using Perl, an open-source programming tool that is freely available via the Internet (www.perl.org). It covers mining ideas from several perspectives--statistics, data mining, linguistics, and information retrieval--and provides readers with the means to successfully complete text mining tasks on their own. The book begins with an introduction to regular expressions, a text pattern methodology, and quantitative text summaries, all of which are fundamental tools of analyzing text. Then, it builds upon this foundation to explore: Probability and texts, including the bag-of-words model Information retrieval techniques such as the TF-IDF similarity measure Concordance lines and corpus linguistics Multivariate techniques such as correlation, principal components analysis, and clustering Perl modules, German, and permutation tests Each chapter is devoted to a single key topic, and the author carefully and thoughtfully introduces mathematical concepts as they arise, allowing readers to learn as they go without having to refer to additional books. The inclusion of numerous exercises and worked-out examples further complements the book's student-friendly format. Practical Text Mining with Perl is ideal as a textbook for undergraduate and graduate courses in text mining and as a reference for a variety of professionals who are interested in extracting information from text documents.

Data Mining and Predictive Analytics

Author: Daniel T. Larose,Chantal D. Larose

Publisher: John Wiley & Sons

ISBN: 1118868676

Category: Computers

Page: 824

View: 641

Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics, Second Edition: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant.com, with exclusive password-protected instructor content Data Mining and Predictive Analytics, Second Edition will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.

Modeling Techniques in Predictive Analytics

Business Problems and Solutions with R

Author: Thomas W. Miller

Publisher: Pearson Education

ISBN: 0133886018

Category: Business & Economics

Page: 359

View: 3335

Today, successful firms win by understanding their data more deeply than competitors do. In short, they compete based on analytics. Now, in Modeling Techniques in Predictive Analytics, the leader of Northwestern University's prestigious analytics program brings together all the concepts, techniques, and R code you need to excel in analytics. Thomas W. Miller's unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike.--

Data Mining and Learning Analytics

Applications in Educational Research

Author: Samira ElAtia,Donald Ipperciel,Osmar R. Zaà ̄ane

Publisher: John Wiley & Sons

ISBN: 1118998219

Category: Computers

Page: 320

View: 2939

Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining’s four guiding principles— prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDM’s emerging role in helping to advance educational research—from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields. Includes case studies where data mining techniques have been effectively applied to advance teaching and learning Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research.

Data Mining and Predictive Analytics

Author: Daniel T. Larose,Chantal D. Larose

Publisher: John Wiley & Sons

ISBN: 1118868706

Category: Computers

Page: 824

View: 9860

Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics, Second Edition: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant.com, with exclusive password-protected instructor content Data Mining and Predictive Analytics, Second Edition will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.

Discovering Knowledge in Data

An Introduction to Data Mining

Author: Daniel T. Larose

Publisher: John Wiley & Sons

ISBN: 1118873572

Category: Computers

Page: 336

View: 314

The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before. This book provides the tools needed to thrive in today’s big data world. The author demonstrates how to leverage a company’s existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will “learn data mining by doing data mining”. By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining. The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis. Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and Visualization Offers extensive coverage of the R statistical programming language Contains 280 end-of-chapter exercises Includes a companion website for university instructors who adopt the book

Data Mining in Grid Computing Environments

Author: Werner Dubitzky

Publisher: John Wiley & Sons

ISBN: 0470699892

Category: Medical

Page: 288

View: 4292

Based around eleven international real life case studies and including contributions from leading experts in the field this groundbreaking book explores the need for the grid-enabling of data mining applications and provides a comprehensive study of the technology, techniques and management skills necessary to create them. This book provides a simultaneous design blueprint, user guide, and research agenda for current and future developments and will appeal to a broad audience; from developers and users of data mining and grid technology, to advanced undergraduate and postgraduate students interested in this field.

Knowledge Discovery Practices and Emerging Applications of Data Mining: Trends and New Domains

Trends and New Domains

Author: Kumar, A.V. Senthil

Publisher: IGI Global

ISBN: 160960069X

Category: Computers

Page: 414

View: 8695

Knowledge Discovery Practices and Emerging Applications of Data Mining: Trends and New Domains introduces the reader to recent research activities in the field of data mining. This book covers association mining, classification, mobile marketing, opinion mining, microarray data mining, internet mining and applications of data mining on biological data, telecommunication and distributed databases, among others, while promoting understanding and implementation of data mining techniques in emerging domains.

Bioinformatics

A Practical Guide to the Analysis of Genes and Proteins

Author: Andreas D. Baxevanis,B. F. Francis Ouellette

Publisher: John Wiley & Sons

ISBN: 0471461016

Category: Computers

Page: 504

View: 9003

"In this book, Andy Baxevanis and Francis Ouellette . . . have undertaken the difficult task of organizing the knowledge in this field in a logical progression and presenting it in a digestible form. And they have done an excellent job. This fine text will make a major impact on biological research and, in turn, on progress in biomedicine. We are all in their debt." —Eric Lander from the Foreword Reviews from the First Edition "...provides a broad overview of the basic tools for sequence analysis ... For biologists approaching this subject for the first time, it will be a very useful handbook to keep on the shelf after the first reading, close to the computer." —Nature Structural Biology "...should be in the personal library of any biologist who uses the Internet for the analysis of DNA and protein sequence data." —Science "...a wonderful primer designed to navigate the novice through the intricacies of in scripto analysis ... The accomplished gene searcher will also find this book a useful addition to their library ... an excellent reference to the principles of bioinformatics." —Trends in Biochemical Sciences This new edition of the highly successful Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins provides a sound foundation of basic concepts, with practical discussions and comparisons of both computational tools and databases relevant to biological research. Equipping biologists with the modern tools necessary to solve practical problems in sequence data analysis, the Second Edition covers the broad spectrum of topics in bioinformatics, ranging from Internet concepts to predictive algorithms used on sequence, structure, and expression data. With chapters written by experts in the field, this up-to-date reference thoroughly covers vital concepts and is appropriate for both the novice and the experienced practitioner. Written in clear, simple language, the book is accessible to users without an advanced mathematical or computer science background. This new edition includes: All new end-of-chapter Web resources, bibliographies, and problem sets Accompanying Web site containing the answers to the problems, as well as links to relevant Web resources New coverage of comparative genomics, large-scale genome analysis, sequence assembly, and expressed sequence tags A glossary of commonly used terms in bioinformatics and genomics Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, Second Edition is essential reading for researchers, instructors, and students of all levels in molecular biology and bioinformatics, as well as for investigators involved in genomics, positional cloning, clinical research, and computational biology.

Managing Data in Motion

Data Integration Best Practice Techniques and Technologies

Author: April Reeve

Publisher: Newnes

ISBN: 0123977916

Category: Computers

Page: 204

View: 1272

Managing Data in Motion describes techniques that have been developed for significantly reducing the complexity of managing system interfaces and enabling scalable architectures. Author April Reeve brings over two decades of experience to present a vendor-neutral approach to moving data between computing environments and systems. Readers will learn the techniques, technologies, and best practices for managing the passage of data between computer systems and integrating disparate data together in an enterprise environment. The average enterprise's computing environment is comprised of hundreds to thousands computer systems that have been built, purchased, and acquired over time. The data from these various systems needs to be integrated for reporting and analysis, shared for business transaction processing, and converted from one format to another when old systems are replaced and new systems are acquired. The management of the "data in motion" in organizations is rapidly becoming one of the biggest concerns for business and IT management. Data warehousing and conversion, real-time data integration, and cloud and "big data" applications are just a few of the challenges facing organizations and businesses today. Managing Data in Motion tackles these and other topics in a style easily understood by business and IT managers as well as programmers and architects. Presents a vendor-neutral overview of the different technologies and techniques for moving data between computer systems including the emerging solutions for unstructured as well as structured data types Explains, in non-technical terms, the architecture and components required to perform data integration Describes how to reduce the complexity of managing system interfaces and enable a scalable data architecture that can handle the dimensions of "Big Data"

Text Mining and Analysis

Practical Methods, Examples, and Case Studies Using SAS

Author: Dr. Goutam Chakraborty,Murali Pagolu,Satish Garla

Publisher: SAS Institute

ISBN: 1612907873

Category: Mathematics

Page: 340

View: 4964

Big data: It's unstructured, it's coming at you fast, and there's lots of it. In fact, the majority of big data is text-oriented, thanks to the proliferation of online sources such as blogs, emails, and social media. However, having big data means little if you can't leverage it with analytics. Now you can explore the large volumes of unstructured text data that your organization has collected with Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS. This hands-on guide to text analytics using SAS provides detailed, step-by-step instructions and explanations on how to mine your text data for valuable insight. Through its comprehensive approach, you'll learn not just how to analyze your data, but how to collect, cleanse, organize, categorize, explore, and interpret it as well. Text Mining and Analysis also features an extensive set of case studies, so you can see examples of how the applications work with real-world data from a variety of industries. Text analytics enables you to gain insights about your customers' behaviors and sentiments. Leverage your organization's text data, and use those insights for making better business decisions with Text Mining and Analysis. This book is part of the SAS Press program.

Introduction to Data Mining

Author: Pang-Ning Tan,Michael Steinbach,Anuj Karpatne,Vipin Kumar

Publisher: Addison-Wesley

ISBN: 9780133128901

Category: Computers

Page: 864

View: 8841

Introducing the fundamental concepts and algorithms of data mining Introduction to Data Mining, 2nd Edition , gives a comprehensive overview of the background and general themes of data mining and is designed to be useful to students, instructors, researchers, and professionals. Presented in a clear and accessible way, the book outlines fundamental concepts and algorithms for each topic, thus providing the reader with the necessary background for the application of data mining to real problems. The text helps readers understand the nuances of the subject, and includes important sections on classification, association analysis, and cluster analysis. This edition improves on the first iteration of the book, published over a decade ago, by addressing the significant changes in the industry as a result of advanced technology and data growth.

Commercial Data Mining

Processing, Analysis and Modeling for Predictive Analytics Projects

Author: David Nettleton

Publisher: Elsevier

ISBN: 012416658X

Category: Computers

Page: 304

View: 1007

Whether you are brand new to data mining or working on your tenth predictive analytics project, Commercial Data Mining will be there for you as an accessible reference outlining the entire process and related themes. In this book, you'll learn that your organization does not need a huge volume of data or a Fortune 500 budget to generate business using existing information assets. Expert author David Nettleton guides you through the process from beginning to end and covers everything from business objectives to data sources, and selection to analysis and predictive modeling. Commercial Data Mining includes case studies and practical examples from Nettleton's more than 20 years of commercial experience. Real-world cases covering customer loyalty, cross-selling, and audience prediction in industries including insurance, banking, and media illustrate the concepts and techniques explained throughout the book. Illustrates cost-benefit evaluation of potential projects Includes vendor-agnostic advice on what to look for in off-the-shelf solutions as well as tips on building your own data mining tools Approachable reference can be read from cover to cover by readers of all experience levels Includes practical examples and case studies as well as actionable business insights from author's own experience

Handbook of Statistical Analysis and Data Mining Applications

Author: Robert Nisbet,Gary Miner,Ken Yale

Publisher: Elsevier

ISBN: 0124166458

Category: Mathematics

Page: 822

View: 8728

Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. Includes input by practitioners for practitioners Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models Contains practical advice from successful real-world implementations Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications

Intelligent Natural Language Processing: Trends and Applications

Author: Khaled Shaalan,Aboul Ella Hassanien,Fahmy Tolba

Publisher: Springer

ISBN: 3319670565

Category: Computers

Page: 776

View: 5549

This book brings together scientists, researchers, practitioners, and students from academia and industry to present recent and ongoing research activities concerning the latest advances, techniques, and applications of natural language processing systems, and to promote the exchange of new ideas and lessons learned. Taken together, the chapters of this book provide a collection of high-quality research works that address broad challenges in both theoretical and applied aspects of intelligent natural language processing. The book presents the state-of-the-art in research on natural language processing, computational linguistics, applied Arabic linguistics and related areas. New trends in natural language processing systems are rapidly emerging – and finding application in various domains including education, travel and tourism, and healthcare, among others. Many issues encountered during the development of these applications can be resolved by incorporating language technology solutions. The topics covered by the book include: Character and Speech Recognition; Morphological, Syntactic, and Semantic Processing; Information Extraction; Information Retrieval and Question Answering; Text Classification and Text Mining; Text Summarization; Sentiment Analysis; Machine Translation Building and Evaluating Linguistic Resources; and Intelligent Language Tutoring Systems.

Scientific Data Mining

A Practical Perspective

Author: Chandrika Kamath

Publisher: SIAM

ISBN: 0898716756

Category: Mathematics

Page: 286

View: 6326

Chandrika Kamath describes how techniques from the multi-disciplinary field of data mining can be used to address the modern problem of data overload in science and engineering domains. Starting with a survey of analysis problems in different applications, it identifies the common themes across these domains.

Data Mining Applications with R

Author: Yanchang Zhao,Yonghua Cen

Publisher: Academic Press

ISBN: 0124115209

Category: Computers

Page: 514

View: 6777

Data Mining Applications with R is a great resource for researchers and professionals to understand the wide use of R, a free software environment for statistical computing and graphics, in solving different problems in industry. R is widely used in leveraging data mining techniques across many different industries, including government, finance, insurance, medicine, scientific research and more. This book presents 15 different real-world case studies illustrating various techniques in rapidly growing areas. It is an ideal companion for data mining researchers in academia and industry looking for ways to turn this versatile software into a powerful analytic tool. R code, Data and color figures for the book are provided at the RDataMining.com website. Helps data miners to learn to use R in their specific area of work and see how R can apply in different industries Presents various case studies in real-world applications, which will help readers to apply the techniques in their work Provides code examples and sample data for readers to easily learn the techniques by running the code by themselves

Automated Data Collection with R

A Practical Guide to Web Scraping and Text Mining

Author: Simon Munzert,Christian Rubba,Peter Meißner,Dominic Nyhuis

Publisher: John Wiley & Sons

ISBN: 111883481X

Category: COMPUTERS

Page: 480

View: 4096

"This book provides a unified framework of web scraping and information extraction from text data with R for the social sciences"--

Tech Mining

Exploiting New Technologies for Competitive Advantage

Author: Alan L. Porter,Scott W. Cunningham

Publisher: John Wiley & Sons

ISBN: 0471698458

Category: Technology & Engineering

Page: 384

View: 3313

Tech Mining makes exploitation of text databases meaningful to those who can gain from derived knowledge about emerging technologies. It begins with the premise that we have the information, the tools to exploit it, and the need for the resulting knowledge. The information provided puts new capabilities at the hands of technology managers. Using the material present, these managers can identify and access the most valuable technology information resources (publications, patents, etc.); search, retrieve, and clean the information on topics of interest; and lower the costs and enhance the benefits of competitive technological intelligence operations.

Permutation, Parametric, and Bootstrap Tests of Hypotheses

Author: Phillip I. Good

Publisher: Springer Science & Business Media

ISBN: 0387271589

Category: Mathematics

Page: 316

View: 9736

Previous edition sold over 1400 copies worldwide. This new edition includes many more real-world illustrations from biology, business, clinical trials, economics, geology, law, medicine, social science and engineering along with twice the number of exercises.