Articles by "Data Mining"

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Galit Shmueli, Peter C. Bruce, Nitin R. Patel ... 560 pages - Publisher: Wiley; 3rd edition (April, 2016) ... Language: English - ISBN-10: 1118729277 - ISBN-13: 978-1118729274.

Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition presents 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.

Max Bramer ... 544 pages - Publisher: Springer; 3rd edition (November, 2016) ... Language: English - AmazonSIN: B01N3LZ1KI.

This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering. Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail. It can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science. As an aid to self study, this book aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included. This expanded third edition includes detailed descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift.

Zekai Sen ... 277 pages - Publisher: Springer; (March, 2019) ... Language: English - ASIN: B07Q7NZPRR by Amazon.

This book is designed to provide easy means of problem solving based on the science philosophical and logical rules that lead to effective and reliable software at the service of professional earth system scientists through numerical scientific computation techniques. Through careful examination of software illuminated by brief scientific explanations given in the book the reader may develop his/her skills of computer program writing. Science aspects that are concerned with earth systems need numerical computation procedures and algorithms of data collected from the field measurements or laboratory records. The same is also valid for data processing in social sciences and economics. Some of the data assessment and processing procedures are at the large scales and complex, and therefore, require effective and efficient computer programs. Data reduction and graphical display in addition to probabilistic and statistical calculations are among the general purposes of the book. Not only students’ works but also projects of researchers at universities and tasks of experts in different companies depend on reliable software. Especially, potential users of MATLAB in earth systems need a guidance book that covers a variety of practically applicable software solutions.

Farhad Hosseinzadeh Lotfi, Ali Ebrahimnejad, Mohsen Vaez-Ghasemi, Zohreh Moghaddas ... 236 pages - Publisher: Springer; (July, 2019) ... Language: English - ASIN: B07VPCDJL5 by Amazon.

This book introduces readers to the use of R codes for optimization problems. First, it provides the necessary background to understand data envelopment analysis (DEA), with a special emphasis on fuzzy DEA. It then describes DEA models, including fuzzy DEA models, and shows how to use them to solve optimization problems with R. Further, it discusses the main advantages of R in optimization problems, and provides R codes based on real-world data sets throughout. Offering a comprehensive review of DEA and fuzzy DEA models and the corresponding R codes, this practice-oriented reference guide is intended for masters and Ph.D. students in various disciplines, as well as practitioners and researchers.

Xin-She Yang ... 175 pages - Publisher: Academic Press; (June, 2019) ... Language: English - ASIN: B07T7VSR37 by Amazon.

Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions.

Features: Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data. + Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics. + Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-study. + Provides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages.

Bertrand Clarke, Ernest Fokoue, Hao Helen Zhang ... 786 pages - Publisher: Springer; (July, 2009) ... Language: English - ISBN-10: 0387981349 - ISBN-13: 978-0387981345. 

This book is a thorough introduction to the most important topics in data mining and machine learning. It begins with a detailed review of classical function estimation and proceeds with chapters on nonlinear regression, classification, and ensemble methods. The final chapters focus on clustering, dimension reduction, variable selection, and multiple comparisons. All these topics have undergone extraordinarily rapid development in recent years and this treatment offers a modern perspective emphasizing the most recent contributions. The presentation of foundational results is detailed and includes many accessible proofs not readily available outside original sources. While the orientation is conceptual and theoretical, the main points are regularly reinforced by computational comparisons.

Intended primarily as a graduate level textbook for statistics, computer science, and electrical engineering students, this book assumes only a strong foundation in undergraduate statistics and mathematics, and facility with using R packages. The text has a wide variety of problems, many of an exploratory nature. There are numerous computed examples, complete with code, so that further computations can be carried out readily. The book also serves as a handbook for researchers who want a conceptual overview of the central topics in data mining and machine learning.

David L. Olson, Georg Lauhoff ... 130 pages - Publisher: Springer; 2nd edition (May, 2019) ... Language: English - AmazonSIN: B07RJZD949.

This book provides an overview of data mining methods demonstrated by software. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools. Descriptive analytics focus on reports of what has happened. Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability. It also includes classification modeling. Diagnostic analytics can apply analysis to sensor input to direct control systems automatically. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems. Data mining includes descriptive and predictive modeling. Operations research includes all three. This book focuses on descriptive analytics. The book seeks to provide simple explanations and demonstration of some descriptive tools. This second edition provides more examples of big data impact, updates the content on visualization, clarifies some points, and expands coverage of association rules and cluster analysis. Chapter 1 gives an overview in the context of knowledge management. Chapter 2 discusses some basic software support to data visualization. Chapter 3 covers fundamentals of market basket analysis, and Chapter 4 provides demonstration of RFM modeling, a basic marketing data mining tool. Chapter 5 demonstrates association rule mining. Chapter 6 is a more in-depth coverage of cluster analysis. Chapter 7 discusses link analysis.  Models are demonstrated using business related data. The style of the book is intended to be descriptive, seeking to explain how methods work, with some citations, but without deep scholarly reference. The data sets and software are all selected for widespread availability and access by any reader with computer links.

Mehmed Kantardzic ... 672 pages - Publisher: Wiley-IEEE Press; 3rd edition (November, 2019) ... Language: English - ISBN-10: 1119516048 - ISBN-13: 978-1119516040.

Presents the latest techniques for analyzing and extracting information from large amounts of data in high-dimensional data spaces: The revised and updated third edition of Data Mining contains in one volume an introduction to a systematic approach to the analysis of large data sets that integrates results from disciplines such as statistics, artificial intelligence, data bases, pattern recognition, and computer visualization. Advances in deep learning technology have opened an entire new spectrum of applications. The author―a noted expert on the topic―explains the basic concepts, models, and methodologies that have been developed in recent years. This new edition introduces and expands on many topics, as well as providing revised sections on software tools and data mining applications. Additional changes include an updated list of references for further study, and an extended list of problems and questions that relate to each chapter.This third edition presents new and expanded information that: • Explores big data and cloud computing • Examines deep learning • Includes information on convolutional neural networks (CNN) • Offers reinforcement learning • Contains semi-supervised learning and S3VM • Reviews model evaluation for unbalanced data. Written for graduate students in computer science, computer engineers, and computer information systems professionals, the updated third edition of Data Mining continues to provide an essential guide to the basic principles of the technology and the most recent developments in the field.

Bruce Ratner ... 724 pages - Publisher: Chapman and Hall/CRC; 3rd edition (June, 2017) ... Language: English - ISBN-10: 9781498797603 - ISBN-13: 978-1498797603. 

Interest in predictive analytics of big data has grown exponentially in the four years since the publication of Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition. In the third edition of this bestseller, the author has completely revised, reorganized, and repositioned the original chapters and produced 13 new chapters of creative and useful machine-learning data mining techniques. In sum, the 43 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature.

What is new in the Third Edition: The current chapters have been completely rewritten. + The core content has been extended with strategies and methods for problems drawn from the top predictive analytics conference and statistical modeling workshops. + Adds thirteen new chapters including coverage of data science and its rise, market share estimation, share of wallet modeling without survey data, latent market segmentation, statistical regression modeling that deals with incomplete data, decile analysis assessment in terms of the predictive power of the data, and a user-friendly version of text mining, not requiring an advanced background in natural language processing (NLP). + Includes SAS subroutines which can be easily converted to other languages. As in the previous edition, this book offers detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. The author addresses each methodology and assigns its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.

Yong Shi, Yingjie Tian, Gang Kou, Yi Peng, Jianping Li ... 316 pages - Publisher: Springer; (May, 2011) ... Language: English - ASIN: B00F5QT36Q by Amazon.

Optimization techniques have been widely adopted to implement various data mining algorithms. In addition to well-known Support Vector Machines (SVMs) (which are based on quadratic programming), different versions of Multiple Criteria Programming (MCP) have been extensively used in data separations. Since optimization based data mining methods differ from statistics, decision tree induction, and neural networks, their theoretical inspiration has attracted many researchers who are interested in algorithm development of data mining.

Optimization based Data Mining: Theory and Applications, mainly focuses on MCP and SVM especially their recent theoretical progress and real-life applications in various fields. These include finance, web services, bio-informatics and petroleum engineering, which has triggered the interest of practitioners who look for new methods to improve the results of data mining for knowledge discovery. Most of the material in this book is directly from the research and application activities that the authors’ research group has conducted over the last ten years. Aimed at practitioners and graduates who have a fundamental knowledge in data mining, it demonstrates the basic concepts and foundations on how to use optimization techniques to deal with data mining problems.

David L. Olson, Desheng Wu ... 102 pages - Publisher: Springer; (September, 2016) ... Language: English - ASIN: B01LWORTEH by Amazon.

This book reviews forecasting data mining models, from basic tools for stable data through causal models, to more advanced models using trends and cycles. These models are demonstrated on the basis of business-related data, including stock indices, crude oil prices, and the price of gold. The book’s main approach is above all descriptive, seeking to explain how the methods concretely work; as such, it includes selected citations, but does not go into deep scholarly reference. The data sets and software reviewed were selected for their widespread availability to all readers with internet access.

Daniel J. Denis ... 760 pages - Publisher: Wiley; (December, 2015) ... Language: English - ISBN-10: 9781118632338 - ISBN-13: 978-1118632338 ...

A clear and efficient balance between theory and application of statistical modeling techniques in the social and behavioral sciences: Written as a general and accessible introduction, Applied Univariate, Bivariate, and Multivariate Statistics provides an overview of statistical modeling techniques used in fields in the social and behavioral sciences. Blending statistical theory and methodology, the book surveys both the technical and theoretical aspects of good data analysis. Featuring applied resources at various levels, the book includes statistical techniques such as t-tests and correlation as well as more advanced procedures such as MANOVA, factor analysis, and structural equation modeling. To promote a more in-depth interpretation of statistical techniques across the sciences, the book surveys some of the technical arguments underlying formulas and equations. Applied Univariate, Bivariate, and Multivariate Statistics also features: Demonstrations of statistical techniques using software packages such as R and SPSS + Examples of hypothetical and real data with subsequent statistical analyses + Historical and philosophical insights into many of the techniques used in modern social science + A companion website that includes further instructional details, additional data sets, solutions to selected exercises, and multiple programming options. An ideal textbook for courses in statistics and methodology at the upper- undergraduate and graduate-levels in psychology, political science, biology, sociology, education, economics, communications, law, and survey research, Applied Univariate, Bivariate, and Multivariate Statistics is also a useful reference for practitioners and researchers in their field of application.

David L. Olson ... 173 pages - Publisher: Business Expert Press; (June, 2016) ... Language: English - ISBN-10: 9781631575488 - ISBN-13: 978-1631575488 ...

Data mining has become the fastest growing topic of interest in business programs in the past decade. This book is intended to describe the benefits of data mining in business, the process and typical business applications, the workings of basic data mining models, and demonstrate each with widely available free software. The book focuses on demonstrating common business data mining applications. It provides exposure to the data mining process, to include problem identification, data management, and available modeling tools. The book takes the approach of demonstrating typical business data sets with open source software. KNIME is a very easy-to-use tool, and is used as the primary means of demonstration. R is much more powerful and is a commercially viable data mining tool. We also demonstrate WEKA, which is a highly useful academic software, although it is difficult to manipulate test sets and new cases, making it problematic for commercial use.

Johan A. K. Suykens, Marco Signoretto ... 525 pages - Publisher: Chapman and Hall/CRC; (October, 2014) ... Language: English - ISBN-10: 1482241390 - ISBN-13: 978-1482241396 ...

Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vector machines. Consisting of 21 chapters authored by leading researchers in machine learning, this comprehensive reference: Covers the relationship between support vector machines (SVMs) and the Lasso * Discusses multi-layer SVMs * Explores nonparametric feature selection, basis pursuit methods, and robust compressive sensing * Describes graph-based regularization methods for single- and multi-task learning * Considers regularized methods for dictionary learning and portfolio selection * Addresses non-negative matrix factorization * Examines low-rank matrix and tensor-based models * Presents advanced kernel methods for batch and online machine learning, system identification, domain adaptation, and image processing * Tackles large-scale algorithms including conditional gradient methods, (non-convex) proximal techniques, and stochastic gradient descent. Regularization, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing, statistical learning, and related areas.

Jian Qing Shi, Taeryon Choi ... 216 pages - Publisher: CRC; (July, 2011) .. Language: English - ISBN-10: 1439837732 - ISBN-13: 978-1439837733 ...

Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors focus on problems involving functional response variables and mixed covariates of functional and scalar variables. Covering the basics of Gaussian process regression, the first several chapters discuss functional data analysis, theoretical aspects based on the asymptotic properties of Gaussian process regression models, and new methodological developments for high dimensional data and variable selection. The remainder of the text explores advanced topics of functional regression analysis, including novel nonparametric statistical methods for curve prediction, curve clustering, functional ANOVA, and functional regression analysis of batch data, repeated curves, and non-Gaussian data. Many flexible models based on Gaussian processes provide efficient ways of model learning, interpreting model structure, and carrying out inference, particularly when dealing with large dimensional functional data. This book shows how to use these Gaussian process regression models in the analysis of functional data. Some MATLAB® and C codes are available on the first author’s website.

Samprit Chatterjee, Ali S. Hadi ... 424 pages - Publisher: Wiley; 5th edition (September, 2012) ... Language: English - ISBN-10: 0470905840 - ISBN-13: 978-0470905845...

Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however,requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded and thoroughly updated to reflect recent advances in the field. The emphasis continues to be on exploratory data analysis rather than statistical theory. The book offers in-depth treatment of regression diagnostics,transformation, multicollinearity, logistic regression, and robust regression. The book now includes a new chapter on the detection and correction of multicollinearity, while also showcasing the use of the discussed methods on newly added data sets from the fields of engineering, medicine, and business. The Fifth Edition also explores additional topics, including: Surrogate ridge regression * Fitting nonlinear models * Errors in variables * ANOVA for designed experiments. Methods of regression analysis are clearly demonstrated, and examples containing the types of irregularities commonly encountered in the real world are provided. Each example isolates one or two techniques and features detailed discussions, the required assumptions, and the evaluated success of each technique.Additionally, methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software package R.

Robert Nisbet, Gary Miner, Ken Yale ... 822 pages - Publisher: Academic Press; 2nd edition (November, 2017) ... Language: English - ISBN-10: 0124166326 - ISBN-13: 978-0124166325 ...

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.

Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal ... 655 pages - Publisher: Morgan Kaufmann; 4th edition (October, 2016) ... Language: English - ASIN: B01MG31RL3 by Amazon ...

Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research.

Petros Xanthopoulos, Panos M. Pardalos, Theodore B. Trafalis ... 59 pages - Publisher: Springer New York; (November, 2012) ... Language: English - ASIN: B00AYR26DI by Amazon

Data uncertainty is a concept closely related with most real life applications that involve data collection and interpretation. Examples can be found in data acquired with biomedical instruments or other experimental techniques. Integration of robust optimization in the existing data mining techniques aim to create new algorithms resilient to error and noise.This work encapsulates all the latest applications of robust optimization in data mining. This brief contains an overview of the rapidly growing field of robust data mining research field and presents the most well known machine learning algorithms, their robust counterpart formulations and algorithms for attacking these problems. This brief will appeal to theoreticians and data miners working in this field.

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