Introduction To Business Data Mining

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Introduction To Business Data Mining

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Author : David Louis Olson
language : en
Publisher:
Release Date : 2007

Introduction To Business Data Mining written by David Louis Olson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Business categories.

Introduction To Business Data Mining

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Author : David Olson
language : en
Publisher:
Release Date : 2005

Introduction To Business Data Mining written by David Olson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.

Introduction to Business Data Mining was developed to introduce students, as opposed to professional practitioners or engineering students, to the fundamental concepts of data mining. Most importantly, this text shows readers how to gather and analyze large sets of data to gain useful business understanding. A four part organization introduces the material (Part I), describes and demonstrated basic data mining algorithms (Part II), focuses on the business applications of data mining (Part III), and presents an overview of the developing areas in this field, including web mining, text mining.

Data Mining For Business Analytics

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Author : Galit Shmueli
language : en
Publisher: John Wiley & Sons
Release Date : 2016-05-11

Data Mining For Business Analytics written by Galit Shmueli and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-05-11 with Mathematics categories.

Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® presents an applied and interactive approach to data mining. Featuring hands-on applications with JMP Pro®, a statistical package from the SAS Institute, the book uses engaging, real-world examples to build a theoretical and practical understanding of key data mining methods, especially predictive models for classification and prediction. Topics include data visualization, dimension reduction techniques, clustering, linear and logistic regression, classification and regression trees, discriminant analysis, naive Bayes, neural networks, uplift modeling, ensemble models, and time series forecasting. Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® also includes: Detailed summaries that supply an outline of key topics at the beginning of each chapter End-of-chapter examples and exercises that allow readers to expand their comprehension of the presented material Data-rich case studies to illustrate various applications of data mining techniques A companion website with over two dozen data sets, exercises and case study solutions, and slides for instructors www.dataminingbook.com Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® is an excellent textbook for advanced undergraduate and graduate-level courses on data mining, predictive analytics, and business analytics. The book is also a one-of-a-kind resource for data scientists, analysts, researchers, and practitioners working with analytics in the fields of management, finance, marketing, information technology, healthcare, education, and any other data-rich field.

Data And Text Mining A Business Applications Approach

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Author : Miller
language : en
Publisher: Pearson Education India
Release Date : 2008-09

Data And Text Mining A Business Applications Approach written by Miller and has been published by Pearson Education India this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-09 with categories.

Introduction To Business Analytics

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Author : Dwaipayan Sethi
language : en
Publisher: Educohack Press
Release Date : 2025-02-20

Introduction To Business Analytics written by Dwaipayan Sethi and has been published by Educohack Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-20 with Business & Economics categories.

"Introduction to Business Analytics" delves into the convergence of data and technology in our digitized world. We explore the significance of business analytics, a field that uses data, statistical analysis, and data mining to understand and improve business performance. We examine how business analytics tools help predict business outcomes by visualizing trends and patterns through forecasting and predictive modeling. These tools are integral to business success, aiding companies in making data-driven decisions. In many contexts, data analysis is the first step in solving business problems, making it crucial for business students to grasp these concepts. This book provides insights into the meaning, scope, tools, and methods of business analytics. We explore advanced business analytics tools used for data analysis and aim to be the primary resource for students, researchers, and anyone interested in the field. The first section covers the foundation of business analytics, including descriptive, predictive, and prescriptive analytics. With simple and easy-to-understand explanations, we cover both basic and advanced business analytics concepts.

Data Mining For Business Intelligence

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Author : Galit Shmueli
language : en
Publisher: John Wiley & Sons
Release Date : 2011-09-28

Data Mining For Business Intelligence written by Galit Shmueli and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-28 with Mathematics categories.

Praise for the First Edition " full of vivid and thought-provoking anecdotes needs to be read by anyone with a serious interest in research and marketing." —Research magazine "Shmueli et al. have done a wonderful job in presenting the field of data mining a welcome addition to the literature." —computingreviews.com Incorporating a new focus on data visualization and time series forecasting, Data Mining for Business Intelligence, Second Edition continues to supply insightful, detailed guidance on fundamental data mining techniques. This new edition guides readers through the use of the Microsoft Office Excel add-in XLMiner for developing predictive models and techniques for describing and finding patterns in data. From clustering customers into market segments and finding the characteristics of frequent flyers to learning what items are purchased with other items, the authors use interesting, real-world examples to build a theoretical and practical understanding of key data mining methods, including classification, prediction, and affinity analysis as well as data reduction, exploration, and visualization. The Second Edition now features: Three new chapters on time series forecasting, introducing popular business forecasting methods including moving average, exponential smoothing methods; regression-based models; and topics such as explanatory vs. predictive modeling, two-level models, and ensembles A revised chapter on data visualization that now features interactive visualization principles and added assignments that demonstrate interactive visualization in practice Separate chapters that each treat k-nearest neighbors and Naïve Bayes methods Summaries at the start of each chapter that supply an outline of key topics The book includes access to XLMiner, allowing readers to work hands-on with the provided data. Throughout the book, applications of the discussed topics focus on the business problem as motivation and avoid unnecessary statistical theory. Each chapter concludes with exercises that allow readers to assess their comprehension of the presented material. The final chapter includes a set of cases that require use of the different data mining techniques, and a related Web site features data sets, exercise solutions, PowerPoint slides, and case solutions. Data Mining for Business Intelligence, Second Edition is an excellent book for courses on data mining, forecasting, and decision support systems at the upper-undergraduate and graduate levels. It is also a one-of-a-kind resource for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.

Data And Text Mining

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Author : Thomas W. Miller
language : en
Publisher: Prentice Hall
Release Date : 2005

Data And Text Mining written by Thomas W. Miller and has been published by Prentice Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Business categories.

Using worked examples and business case studies, the text answers the four questions: why is data mining important to business and marketing research; how is data mining different from other types of research; what do we learn from data mining; and how do we do data mining?Contents: 1. What Is Data Mining? 2. Traditional Methods. 3. Data-Adaptive Methods. 4. Text Mining. Appendix A: Business Cases. List of Tables. List of Figures. List of Exhibits.

Applied Data Mining For Business And Industry

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Author : Paolo Giudici
language : en
Publisher: John Wiley & Sons
Release Date : 2009-04-15

Applied Data Mining For Business And Industry written by Paolo Giudici and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-04-15 with Mathematics categories.

The increasing availability of data in our current, information overloaded society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract knowledge from such data. This book provides an accessible introduction to data mining methods in a consistent and application oriented statistical framework, using case studies drawn from real industry projects and highlighting the use of data mining methods in a variety of business applications. Introduces data mining methods and applications. Covers classical and Bayesian multivariate statistical methodology as well as machine learning and computational data mining methods. Includes many recent developments such as association and sequence rules, graphical Markov models, lifetime value modelling, credit risk, operational risk and web mining. Features detailed case studies based on applied projects within industry. Incorporates discussion of data mining software, with case studies analysed using R. Is accessible to anyone with a basic knowledge of statistics or data analysis. Includes an extensive bibliography and pointers to further reading within the text. Applied Data Mining for Business and Industry, 2nd edition is aimed at advanced undergraduate and graduate students of data mining, applied statistics, database management, computer science and economics. The case studies will provide guidance to professionals working in industry on projects involving large volumes of data, such as customer relationship management, web design, risk management, marketing, economics and finance.

Applied Data Mining

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Author : Paolo Giudici
language : en
Publisher: John Wiley & Sons
Release Date : 2005-09-27

Applied Data Mining written by Paolo Giudici and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-09-27 with Computers categories.

Data mining can be defined as the process of selection, explorationand modelling of large databases, in order to discover models andpatterns. The increasing availability of data in the currentinformation society has led to the need for valid tools for itsmodelling and analysis. Data mining and applied statistical methodsare the appropriate tools to extract such knowledge from data.Applications occur in many different fields, including statistics,computer science, machine learning, economics, marketing andfinance. This book is the first to describe applied data mining methodsin a consistent statistical framework, and then show how they canbe applied in practice. All the methods described are eithercomputational, or of a statistical modelling nature. Complexprobabilistic models and mathematical tools are not used, so thebook is accessible to a wide audience of students and industryprofessionals. The second half of the book consists of nine casestudies, taken from the author's own work in industry, thatdemonstrate how the methods described can be applied to realproblems. Provides a solid introduction to applied data mining methods ina consistent statistical framework Includes coverage of classical, multivariate and Bayesianstatistical methodology Includes many recent developments such as web mining,sequential Bayesian analysis and memory based reasoning Each statistical method described is illustrated with real lifeapplications Features a number of detailed case studies based on appliedprojects within industry Incorporates discussion on software used in data mining, withparticular emphasis on SAS Supported by a website featuring data sets, software andadditional material Includes an extensive bibliography and pointers to furtherreading within the text Author has many years experience teaching introductory andmultivariate statistics and data mining, and working on appliedprojects within industry A valuable resource for advanced undergraduate and graduatestudents of applied statistics, data mining, computer science andeconomics, as well as for professionals working in industry onprojects involving large volumes of data - such as in marketing orfinancial risk management.

Business Process Management Workshops

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Author : Michael zur Muehlen
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-05-10

Business Process Management Workshops written by Michael zur Muehlen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-05-10 with Computers categories.

This book constitutes the thoroughly refereed post-workshop proceedings of nine international workshops held in Hoboken, NJ, USA, in conjunction with the 8th International Conference on Business Process Management, BPM 2010, in September 2010. The nine workshops focused on Reuse in Business Process Management (rBPM 2010), Business Process Management and Sustainability (SusBPM 2010), Business Process Design (BPD 2010), Business Process Intelligence (BPI 2010), Cross-Enterprise Collaboration, People, and Work (CEC-PAW 2010), Process in the Large (IW-PL 2010), Business Process Management and Social Software (BPMS2 2010), Event-Driven Business Process Management (edBPM 2010), and Traceability and Compliance of Semi-Structured Processes (TC4SP 2010). In addition, three papers from the special track on Advances in Business Process Education are also included in this volume. The overall 66 revised full papers presented were carefully reviewed and selected from 143 submissions.