Real World Data Mining
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Real World Data Mining
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Author : Dursun Delen
language : en
Publisher: FT Press
Release Date : 2014-12-16
Real World Data Mining written by Dursun Delen and has been published by FT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-16 with Business & Economics categories.
Use the latest data mining best practices to enable timely, actionable, evidence-based decision making throughout your organization! Real-World Data Mining demystifies current best practices, showing how to use data mining to uncover hidden patterns and correlations, and leverage these to improve all aspects of business performance. Drawing on extensive experience as a researcher, practitioner, and instructor, Dr. Dursun Delen delivers an optimal balance of concepts, techniques and applications. Without compromising either simplicity or clarity, he provides enough technical depth to help readers truly understand how data mining technologies work. Coverage includes: processes, methods, techniques, tools, and metrics; the role and management of data; text and web mining; sentiment analysis; and Big Data integration. Throughout, Delen's conceptual coverage is complemented with application case studies (examples of both successes and failures), as well as simple, hands-on tutorials. Real-World Data Mining will be valuable to professionals on analytics teams; professionals seeking certification in the field; and undergraduate or graduate students in any analytics program: concentrations, certificate-based, or degree-based.
Real World Data Mining Applications
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Author : Mahmoud Abou-Nasr
language : en
Publisher: Springer
Release Date : 2014-11-13
Real World Data Mining Applications written by Mahmoud Abou-Nasr and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-13 with Business & Economics categories.
Data mining applications range from commercial to social domains, with novel applications appearing swiftly; for example, within the context of social networks. The expanding application sphere and social reach of advanced data mining raise pertinent issues of privacy and security. Present-day data mining is a progressive multidisciplinary endeavor. This inter- and multidisciplinary approach is well reflected within the field of information systems. The information systems research addresses software and hardware requirements for supporting computationally and data-intensive applications. Furthermore, it encompasses analyzing system and data aspects, and all manual or automated activities. In that respect, research at the interface of information systems and data mining has significant potential to produce actionable knowledge vital for corporate decision-making. The aim of the proposed volume is to provide a balanced treatment of the latest advances and developments in data mining; in particular, exploring synergies at the intersection with information systems. It will serve as a platform for academics and practitioners to highlight their recent achievements and reveal potential opportunities in the field. Thanks to its multidisciplinary nature, the volume is expected to become a vital resource for a broad readership ranging from students, throughout engineers and developers, to researchers and academics.
Knowledge Discovery And Data Mining Challenges And Realities
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Author : Zhu, Xingquan
language : en
Publisher: IGI Global
Release Date : 2007-04-30
Knowledge Discovery And Data Mining Challenges And Realities written by Zhu, Xingquan and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-04-30 with Computers categories.
"This book provides a focal point for research and real-world data mining practitioners that advance knowledge discovery from low-quality data; it presents in-depth experiences and methodologies, providing theoretical and empirical guidance to users who have suffered from underlying low-quality data. Contributions also focus on interdisciplinary collaborations among data quality, data processing, data mining, data privacy, and data sharing"--Provided by publisher.
Practical Data Mining Techniques And Applications
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Author : Ketan Shah
language : en
Publisher:
Release Date : 2023
Practical Data Mining Techniques And Applications written by Ketan Shah and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with Data mining categories.
Data mining techniques and algorithms are extensively used to build real-world applications. A practical approach can be applied to data mining techniques to build applications. Once deployed, an application enables the developers to work on the users' goals and mold the algorithms with respect to users' perspectives. Practical Data Mining Techniques and Applications focuses on various concepts related to data mining and how these techniques can be used to develop and deploy applications. The book provides a systematic composition of fundamental concepts of data mining blended with practical applications. The aim of this book is to provide access to practical data mining applications and techniques to help readers gain an understanding of data mining in practice. Readers also learn how relevant techniques and algorithms are applied to solve problems and to provide solutions to real-world applications in different domains. This book can help academicians to extend their knowledge of the field as well as their understanding of applications based on different techniques to gain greater insight. It can also help researchers with real-world applications by diving deeper into the domain. Computing science students, application developers, and business professionals may also benefit from this examination of applied data science techniques. By highlighting an overall picture of the field, introducing various mining techniques, and focusing on different applications and research directions using these methods, this book can motivate discussions among academics, researchers, professionals, and students to exchange and develop their views regarding the dynamic field that is data mining.
Data Mining In Time Series Databases
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Author : Mark Last
language : en
Publisher: World Scientific
Release Date : 2004
Data Mining In Time Series Databases written by Mark Last and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Mathematics categories.
Adding the time dimension to real-world databases produces Time Series Databases (TSDB) and introduces new aspects and difficulties to data mining and knowledge discovery. This book covers the state-of-the-art methodology for mining time series databases. The novel data mining methods presented in the book include techniques for efficient segmentation, indexing, and classification of noisy and dynamic time series. A graph-based method for anomaly detection in time series is described and the book also studies the implications of a novel and potentially useful representation of time series as strings. The problem of detecting changes in data mining models that are induced from temporal databases is additionally discussed. Contents: A Survey of Recent Methods for Efficient Retrieval of Similar Time Sequences (H M Lie); Indexing of Compressed Time Series (E Fink & K Pratt); Boosting Interval-Based Literal: Variable Length and Early Classification (J J Rodriguez Diez); Segmenting Time Series: A Survey and Novel Approach (E Keogh et al.); Indexing Similar Time Series under Conditions of Noise (M Vlachos et al.); Classification of Events in Time Series of Graphs (H Bunke & M Kraetzl); Median Strings--A Review (X Jiang et al.); Change Detection in Classfication Models of Data Mining (G Zeira et al.). Readership: Graduate students, reseachers and practitioners in the fields of data mining, machine learning, databases and statistics.
Data Mining
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Author : Ciza Thomas
language : en
Publisher: BoD – Books on Demand
Release Date : 2018-08-22
Data Mining written by Ciza Thomas and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-22 with Computers categories.
This book on data mining explores a broad set of ideas and presents some of the state-of-the-art research in this field. The book is triggered by pervasive applications that retrieve knowledge from real-world big data. Data mining finds applications in the entire spectrum of science and technology including basic sciences to life sciences and medicine, to social, economic, and cognitive sciences, to engineering and computers. The chapters discuss various applications and research frontiers in data mining with algorithms and implementation details for use in real-world. This can be through characterization, classification, discrimination, anomaly detection, association, clustering, trend or evolution prediction, etc. The intended audience of this book will mainly consist of researchers, research students, practitioners, data analysts, and business professionals who seek information on the various data mining techniques and their applications.
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.
Predicting Real World Behaviors From Virtual World Data
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Author : Muhammad Aurangzeb Ahmad
language : en
Publisher: Springer
Release Date : 2014-07-24
Predicting Real World Behaviors From Virtual World Data written by Muhammad Aurangzeb Ahmad and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-24 with Computers categories.
There is a growing body of literature that focuses on the similarities and differences between how people behave in the offline world vs. how they behave in these virtual environments. Data mining has aided in discovering interesting insights with respect to how people behave in these virtual environments. The book addresses prediction, mining and analysis of offline characteristics and behaviors from online data and vice versa. Each chapter will focus on a different aspect of virtual worlds to real world prediction e.g., demographics, personality, location, etc.
Principles Of Data Mining
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Author : David J. Hand
language : en
Publisher: MIT Press
Release Date : 2001-08-17
Principles Of Data Mining written by David J. Hand and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-08-17 with Computers categories.
The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
Data Mining For Business Intelligence
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Author : Galit Shmueli
language : en
Publisher: John Wiley and Sons
Release Date : 2011-06-10
Data Mining For Business Intelligence written by Galit Shmueli and has been published by John Wiley and Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-06-10 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.