Introduction To Data Mining With Case Studies

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INTRODUCTION TO DATA MINING WITH CASE STUDIES

G. K. GUPTA
INTRODUCTION TO DATA MINING WITH CASE STUDIES Book PDF

Author : G. K. GUPTA
Publisher : PHI Learning Pvt. Ltd.
Page : 537 pages
File Size : 52,8 Mb
Release : 2014-06-28
Category : Computers
ISBN : 9788120350021

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INTRODUCTION TO DATA MINING WITH CASE STUDIES by G. K. GUPTA Pdf

The field of data mining provides techniques for automated discovery of valuable information from the accumulated data of computerized operations of enterprises. This book offers a clear and comprehensive introduction to both data mining theory and practice. It is written primarily as a textbook for the students of computer science, management, computer applications, and information technology. The book ensures that the students learn the major data mining techniques even if they do not have a strong mathematical background. The techniques include data pre-processing, association rule mining, supervised classification, cluster analysis, web data mining, search engine query mining, data warehousing and OLAP. To enhance the understanding of the concepts introduced, and to show how the techniques described in the book are used in practice, each chapter is followed by one or two case studies that have been published in scholarly journals. Most case studies deal with real business problems (for example, marketing, e-commerce, CRM). Studying the case studies provides the reader with a greater insight into the data mining techniques. The book also provides many examples, review questions, multiple choice questions, chapter-end exercises and a good list of references and Web resources especially those which are easy to understand and useful for students. A number of class projects have also been included.

Data Mining with R

Luis Torgo
Data Mining with R Book PDF

Author : Luis Torgo
Publisher : CRC Press
Page : 426 pages
File Size : 49,7 Mb
Release : 2016-11-30
Category : Business & Economics
ISBN : 9781315399096

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Data Mining with R by Luis Torgo Pdf

Data Mining with R: Learning with Case Studies, Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition, this new edition is divided into two parts. The first part will feature introductory material, including a new chapter that provides an introduction to data mining, to complement the already existing introduction to R. The second part includes case studies, and the new edition strongly revises the R code of the case studies making it more up-to-date with recent packages that have emerged in R. The book does not assume any prior knowledge about R. Readers who are new to R and data mining should be able to follow the case studies, and they are designed to be self-contained so the reader can start anywhere in the document. The book is accompanied by a set of freely available R source files that can be obtained at the book’s web site. These files include all the code used in the case studies, and they facilitate the "do-it-yourself" approach followed in the book. Designed for users of data analysis tools, as well as researchers and developers, the book should be useful for anyone interested in entering the "world" of R and data mining. About the Author Luís Torgo is an associate professor in the Department of Computer Science at the University of Porto in Portugal. He teaches Data Mining in R in the NYU Stern School of Business’ MS in Business Analytics program. An active researcher in machine learning and data mining for more than 20 years, Dr. Torgo is also a researcher in the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) of INESC Porto LA.

R and Data Mining

Yanchang Zhao
R and Data Mining Book PDF

Author : Yanchang Zhao
Publisher : Academic Press
Page : 256 pages
File Size : 50,6 Mb
Release : 2012-12-31
Category : Mathematics
ISBN : 9780123972712

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R and Data Mining by Yanchang Zhao Pdf

R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more. Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation. With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis. Presents an introduction into using R for data mining applications, covering most popular data mining techniques Provides code examples and data so that readers can easily learn the techniques Features case studies in real-world applications to help readers apply the techniques in their work

Introduction to Data Mining and its Applications

S. Sumathi,S.N. Sivanandam
Introduction to Data Mining and its Applications Book PDF

Author : S. Sumathi,S.N. Sivanandam
Publisher : Springer
Page : 828 pages
File Size : 41,9 Mb
Release : 2006-10-12
Category : Computers
ISBN : 9783540343516

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Introduction to Data Mining and its Applications by S. Sumathi,S.N. Sivanandam Pdf

This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in database systems, and presents a broad, yet in-depth overview of the field of data mining. Data mining is a multidisciplinary field, drawing work from areas including database technology, artificial intelligence, machine learning, neural networks, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing and data visualization.

Humanities Data Analysis

Folgert Karsdorp,Mike Kestemont,Allen Riddell
Humanities Data Analysis Book PDF

Author : Folgert Karsdorp,Mike Kestemont,Allen Riddell
Publisher : Princeton University Press
Page : 352 pages
File Size : 52,7 Mb
Release : 2021-01-12
Category : Computers
ISBN : 9780691172361

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Humanities Data Analysis by Folgert Karsdorp,Mike Kestemont,Allen Riddell Pdf

A practical guide to data-intensive humanities research using the Python programming language The use of quantitative methods in the humanities and related social sciences has increased considerably in recent years, allowing researchers to discover patterns in a vast range of source materials. Despite this growth, there are few resources addressed to students and scholars who wish to take advantage of these powerful tools. Humanities Data Analysis offers the first intermediate-level guide to quantitative data analysis for humanities students and scholars using the Python programming language. This practical textbook, which assumes a basic knowledge of Python, teaches readers the necessary skills for conducting humanities research in the rapidly developing digital environment. The book begins with an overview of the place of data science in the humanities, and proceeds to cover data carpentry: the essential techniques for gathering, cleaning, representing, and transforming textual and tabular data. Then, drawing from real-world, publicly available data sets that cover a variety of scholarly domains, the book delves into detailed case studies. Focusing on textual data analysis, the authors explore such diverse topics as network analysis, genre theory, onomastics, literacy, author attribution, mapping, stylometry, topic modeling, and time series analysis. Exercises and resources for further reading are provided at the end of each chapter. An ideal resource for humanities students and scholars aiming to take their Python skills to the next level, Humanities Data Analysis illustrates the benefits that quantitative methods can bring to complex research questions. Appropriate for advanced undergraduates, graduate students, and scholars with a basic knowledge of Python Applicable to many humanities disciplines, including history, literature, and sociology Offers real-world case studies using publicly available data sets Provides exercises at the end of each chapter for students to test acquired skills Emphasizes visual storytelling via data visualizations

Introduction to Data Mining and Analytics

Kris Jamsa
Introduction to Data Mining and Analytics Book PDF

Author : Kris Jamsa
Publisher : Jones & Bartlett Learning
Page : 687 pages
File Size : 40,6 Mb
Release : 2020-02-03
Category : Computers
ISBN : 9781284180909

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Introduction to Data Mining and Analytics by Kris Jamsa Pdf

Data Mining and Analytics provides a broad and interactive overview of a rapidly growing field. The exponentially increasing rate at which data is generated creates a corresponding need for professionals who can effectively handle its storage, analysis, and translation.

Discovering Knowledge in Data

Daniel T. Larose
Discovering Knowledge in Data Book PDF

Author : Daniel T. Larose
Publisher : John Wiley & Sons
Page : 240 pages
File Size : 55,7 Mb
Release : 2005-01-28
Category : Computers
ISBN : 9780471687535

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Discovering Knowledge in Data by Daniel T. Larose Pdf

Learn Data Mining by doing data mining Data mining can be revolutionary-but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets. Employing a "white box" methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include: * Data preprocessing and classification * Exploratory analysis * Decision trees * Neural and Kohonen networks * Hierarchical and k-means clustering * Association rules * Model evaluation techniques Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge. An Instructor's Manual presenting detailed solutions to all the problems in the book is available online.

A Practical Guide to Data Mining for Business and Industry

Andrea Ahlemeyer-Stubbe,Shirley Coleman
A Practical Guide to Data Mining for Business and Industry Book PDF

Author : Andrea Ahlemeyer-Stubbe,Shirley Coleman
Publisher : John Wiley & Sons
Page : 323 pages
File Size : 41,9 Mb
Release : 2014-03-31
Category : Mathematics
ISBN : 9781118763377

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A Practical Guide to Data Mining for Business and Industry by Andrea Ahlemeyer-Stubbe,Shirley Coleman Pdf

Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is complemented by case studies to create a versatile reference book, allowing readers to look for specific methods as well as for specific applications. The book is formatted to allow statisticians, computer scientists, and economists to cross-reference from a particular application or method to sectors of interest.

Knowledge Discovery and Data Mining

O. Maimon,M. Last
Knowledge Discovery and Data Mining Book PDF

Author : O. Maimon,M. Last
Publisher : Springer Science & Business Media
Page : 192 pages
File Size : 42,6 Mb
Release : 2000-12-31
Category : Computers
ISBN : 0792366476

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Knowledge Discovery and Data Mining by O. Maimon,M. Last Pdf

This book presents a specific and unified approach to Knowledge Discovery and Data Mining, termed IFN for Information Fuzzy Network methodology. Data Mining (DM) is the science of modelling and generalizing common patterns from large sets of multi-type data. DM is a part of KDD, which is the overall process for Knowledge Discovery in Databases. The accessibility and abundance of information today makes this a topic of particular importance and need. The book has three main parts complemented by appendices as well as software and project data that are accessible from the book's web site (http://www.eng.tau.ac.iV-maimonlifn-kdg£). Part I (Chapters 1-4) starts with the topic of KDD and DM in general and makes reference to other works in the field, especially those related to the information theoretic approach. The remainder of the book presents our work, starting with the IFN theory and algorithms. Part II (Chapters 5-6) discusses the methodology of application and includes case studies. Then in Part III (Chapters 7-9) a comparative study is presented, concluding with some advanced methods and open problems. The IFN, being a generic methodology, applies to a variety of fields, such as manufacturing, finance, health care, medicine, insurance, and human resources. The appendices expand on the relevant theoretical background and present descriptions of sample projects (including detailed results).

Text Mining and Visualization

Markus Hofmann,Andrew Chisholm
Text Mining and Visualization Book PDF

Author : Markus Hofmann,Andrew Chisholm
Publisher : CRC Press
Page : 337 pages
File Size : 44,9 Mb
Release : 2016-01-05
Category : Business & Economics
ISBN : 9781482237580

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Text Mining and Visualization by Markus Hofmann,Andrew Chisholm Pdf

Text Mining and Visualization: Case Studies Using Open-Source Tools provides an introduction to text mining using some of the most popular and powerful open-source tools: KNIME, RapidMiner, Weka, R, and Python. The contributors-all highly experienced with text mining and open-source software-explain how text data are gathered and processed from a w

Introduction to Data Science

Rafael A. Irizarry
Introduction to Data Science Book PDF

Author : Rafael A. Irizarry
Publisher : CRC Press
Page : 794 pages
File Size : 55,6 Mb
Release : 2019-11-20
Category : Mathematics
ISBN : 9781000708035

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Introduction to Data Science by Rafael A. Irizarry Pdf

Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.

Data Mining for Business Analytics

Galit Shmueli,Peter C. Bruce,Peter Gedeck,Nitin R. Patel
Data Mining for Business Analytics Book PDF

Author : Galit Shmueli,Peter C. Bruce,Peter Gedeck,Nitin R. Patel
Publisher : John Wiley & Sons
Page : 608 pages
File Size : 55,8 Mb
Release : 2019-10-14
Category : Mathematics
ISBN : 9781119549857

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Data Mining for Business Analytics by Galit Shmueli,Peter C. Bruce,Peter Gedeck,Nitin R. Patel Pdf

Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process A new section on ethical issues in data mining Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” —Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R

Data Mining Methods for the Content Analyst

Kalev Leetaru
Data Mining Methods for the Content Analyst Book PDF

Author : Kalev Leetaru
Publisher : Routledge
Page : 122 pages
File Size : 40,6 Mb
Release : 2012
Category : Computers
ISBN : 9780415895132

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Data Mining Methods for the Content Analyst by Kalev Leetaru Pdf

This research reference introduces readers to the data mining technologies available for use in content analysis research. Supporting the increasingly popular trend of employing digital analysis methodologies in the humanities, arts, and social sciences, this work provides crucial answers for researchers who are not familiar with data mining approaches and who do not know what they can do, how they work, or how their strengths and weaknesses match up to the strengths and weaknesses of human coded content analysis data. Offering valuable insights and guidance for using automated analytical techniques in content analysis research, this guide will appeal to both novice and experienced researchers throughout the humanities, arts, and social sciences.

Data Mining and Analysis

Mohammed J. Zaki,Wagner Meira
Data Mining and Analysis Book PDF

Author : Mohammed J. Zaki,Wagner Meira
Publisher : Cambridge University Press
Page : 607 pages
File Size : 49,7 Mb
Release : 2014-05-12
Category : Computers
ISBN : 9780521766333

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Data Mining and Analysis by Mohammed J. Zaki,Wagner Meira Pdf

A comprehensive overview of data mining from an algorithmic perspective, integrating related concepts from machine learning and statistics.

Introduction to Data Mining

Pang-Ning Tan,Michael Steinbach,Vipin Kumar
Introduction to Data Mining Book PDF

Author : Pang-Ning Tan,Michael Steinbach,Vipin Kumar
Publisher : Pearson Education India
Page : 780 pages
File Size : 49,5 Mb
Release : 2016
Category : Electronic
ISBN : 9789332586055

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Introduction to Data Mining by Pang-Ning Tan,Michael Steinbach,Vipin Kumar Pdf

Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. Each major topic is organized into two chapters, beginni