Applications Of Data Mining In E Business And Finance
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Applications Of Data Mining In E Business And Finance
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Author :
language : en
Publisher:
Release Date : 2008
Applications Of Data Mining In E Business And Finance written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Data mining categories.
In spite of the close relationship between research and practice in Data Mining, it is not easy to find information on some of the important issues involved in real world application of DM technology. This book address some of these issues. It is suitable for Data Mining researchers and practitioners.
Applications Of Data Mining In E Business And Finance
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Author : C. Soares
language : en
Publisher: IOS Press
Release Date : 2008-08-07
Applications Of Data Mining In E Business And Finance written by C. Soares and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-08-07 with Business & Economics categories.
The application of Data Mining (DM) technologies has shown an explosive growth in an increasing number of different areas of business, government and science. Two of the most important business areas are finance, in particular in banks and insurance companies, and e-business, such as web portals, e-commerce and ad management services. In spite of the close relationship between research and practice in Data Mining, it is not easy to find information on some of the most important issues involved in real world application of DM technology, from business and data understanding to evaluation and deployment. Papers often describe research that was developed without taking into account constraints imposed by the motivating application. When these issues are taken into account, they are frequently not discussed in detail because the paper must focus on the method. Therefore knowledge that could be useful for those who would like to apply the same approach on a related problem is not shared. The papers in this book address some of these issues. This book is of interest not only to Data Mining researchers and practitioners, but also to students who wish to have an idea of the practical issues involved in Data Mining.
Applications Of Data Mining In E Business And Finance
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Author : Carlos Soares
language : en
Publisher:
Release Date : 2008
Applications Of Data Mining In E Business And Finance written by Carlos Soares and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Business & Economics categories.
Contains extended versions of a selection of papers presented at the workshop Data mining for business, held in 2007 together with the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Nanjing China--Preface.
Applications Of Data Mining In E Business And Finance
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Author : Carlos A. Mota Soares
language : en
Publisher: IOS Press
Release Date : 2008
Applications Of Data Mining In E Business And Finance written by Carlos A. Mota Soares and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Business & Economics categories.
Contains extended versions of a selection of papers presented at the workshop Data mining for business, held in 2007 together with the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Nanjing China--Preface.
Data Mining For Business Analytics
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Author : Galit Shmueli
language : en
Publisher: John Wiley & Sons
Release Date : 2019-11-05
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 2019-11-05 with Mathematics categories.
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 For Business Applications
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Author : Carlos A. Mota Soares
language : en
Publisher: IOS Press
Release Date : 2010
Data Mining For Business Applications written by Carlos A. Mota Soares and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Computers categories.
Data mining is already incorporated into the business processes in sectors such as health, retail, automotive, finance, telecom and insurance as well as in government. This book contains extended versions of a selection of papers presented at a series of workshops held between 2005 and 2008 on the subject of data mining for business applications.
Data Analytics For Business
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Author : Fenio Annansingh
language : en
Publisher: Routledge
Release Date : 2022-04-20
Data Analytics For Business written by Fenio Annansingh and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-20 with Business & Economics categories.
Data analytics underpin our modern data-driven economy. This textbook explains the relevance of data analytics at the firm and industry levels, tracing the evolution and key components of the field, and showing how data analytics insights can be leveraged for business results. The first section of the text covers key topics such as data analytics tools, data mining, business intelligence, customer relationship management, and cybersecurity. The chapters then take an industry focus, exploring how data analytics can be used in particular settings to strengthen business decision-making. A range of sectors are examined, including financial services, accounting, marketing, sport, health care, retail, transport, and education. With industry case studies, clear definitions of terminology, and no background knowledge required, this text supports students in gaining a solid understanding of data analytics and its practical applications. PowerPoint slides, a test bank of questions, and an instructor’s manual are also provided as online supplements. This will be a valuable text for undergraduate level courses in data analytics, data mining, business intelligence, and related areas.
Applications Of Data Mining To Electronic Commerce
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Author : Ronny Kohavi
language : en
Publisher: Springer
Release Date : 2001-02-28
Applications Of Data Mining To Electronic Commerce written by Ronny Kohavi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-02-28 with Business & Economics categories.
Applications of Data Mining to Electronic Commerce brings together in one place important contributions and up-to-date research results in this fast moving area. Applications of Data Mining to Electronic Commerce serves as an excellent reference, providing insight into some of the most challenging research issues in the field.
Data Mining In Finance
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Author : Boris Kovalerchuk
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-12-11
Data Mining In Finance written by Boris Kovalerchuk 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 2005-12-11 with Computers categories.
Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then examines the suitability of these approaches to financial data mining. The book focuses specifically on relational data mining (RDM), which is a learning method able to learn more expressive rules than other symbolic approaches. RDM is thus better suited for financial mining, because it is able to make greater use of underlying domain knowledge. Relational data mining also has a better ability to explain the discovered rules - an ability critical for avoiding spurious patterns which inevitably arise when the number of variables examined is very large. The earlier algorithms for relational data mining, also known as inductive logic programming (ILP), suffer from a relative computational inefficiency and have rather limited tools for processing numerical data. Data Mining in Finance introduces a new approach, combining relational data mining with the analysis of statistical significance of discovered rules. This reduces the search space and speeds up the algorithms. The book also presents interactive and fuzzy-logic tools for `mining' the knowledge from the experts, further reducing the search space. Data Mining in Finance contains a number of practical examples of forecasting S&P 500, exchange rates, stock directions, and rating stocks for portfolio, allowing interested readers to start building their own models. This book is an excellent reference for researchers and professionals in the fields of artificial intelligence, machine learning, data mining, knowledge discovery, and applied mathematics.
Data Mining For Business Analytics
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Author : Galit Shmueli
language : en
Publisher: John Wiley & Sons
Release Date : 2016-04-18
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 2016-04-18 with Mathematics categories.
An applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft® Office Excel® add-in XLMiner® to develop predictive models and learn how to obtain business value from Big Data. Featuring updated topical coverage on text mining, social network analysis, collaborative filtering, ensemble methods, uplift modeling and more, the Third Edition also includes: Real-world examples to build a theoretical and practical understanding of key data mining methods End-of-chapter exercises that help readers better understand the presented material Data-rich case studies to illustrate various applications of data mining techniques Completely new chapters on social network analysis and text mining A companion site with additional data sets, instructors material that include solutions to exercises and case studies, and Microsoft PowerPoint® slides https://www.dataminingbook.com Free 140-day license to use XLMiner for Education software Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology. Praise for the Second 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 "Excellent choice for business analysts...The book is a perfect fit for its intended audience." – Keith McCormick, Consultant and Author of SPSS Statistics For Dummies, Third Edition and SPSS Statistics for Data Analysis and Visualization Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University’s Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, The Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 70 journal articles, books, textbooks and book chapters. Peter C. Bruce is President and Founder of the Institute for Statistics Education at www.statistics.com. He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective, also published by Wiley. Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad for 15 years.