Data Mining and Predictive Analytics


Author: Daniel T. Larose
Publisher: John Wiley & Sons
ISBN: 1118868706
Category: Computers
Page: 824
View: 6830

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Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content Data Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.

Discovering Knowledge in Data

An Introduction to Data Mining
Author: Daniel T. Larose,Chantal D. Larose
Publisher: John Wiley & Sons
ISBN: 1118873572
Category: Computers
Page: 336
View: 5891

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The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before. This book provides the tools needed to thrive in today’s big data world. The author demonstrates how to leverage a company’s existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will “learn data mining by doing data mining”. By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining. The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis. Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and Visualization Offers extensive coverage of the R statistical programming language Contains 280 end-of-chapter exercises Includes a companion website for university instructors who adopt the book

Data Science für Unternehmen

Data Mining und datenanalytisches Denken praktisch anwenden
Author: Foster Provost,Tom Fawcett
Publisher: MITP-Verlags GmbH & Co. KG
ISBN: 3958455484
Category: Computers
Page: 432
View: 1399

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Multivariate Modellierung, Prognose und Evaluation sporadischer Nachfragezeitreihen


Author: Ekaterina Nieberle
Publisher: BoD – Books on Demand
ISBN: 3844104623
Category: Business & Economics
Page: 384
View: 2876

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Prognosen in der Güterwirtschaft dienen der Unterstützung von Entscheidungen über eine akkurate Lagerbevorratung und Disposition auf jeder Stufe der Lieferkette. Eine durch einen hohen Nullanteil ausgezeichnete Zeitreihe liefert wesentlich weniger Information über ihre Verlaufsmuster als eine glatte Zeitreihe mit quasi-stetigem Wertebereich, so dass Identifikation und akkurate Schätzung der Strukturen sporadischer Zeitreihen auf der Item-Ebene nicht möglich sind. Häufig lassen sich keine strukturellen Unterschiede zwischen einzelnen sporadischen Zeitreihen erkennen. Betrachtet man dagegen eine Gruppe von Zeitreihen mit ähnlichen Strukturverläufen, so sind die gemeinsamen Strukturen visuell erkennbar. Die Identifikation der für mehrere Zeitreihen gemeinsamen Strukturen und deren Schätzung mit Hilfe multivariater Paneldatenmodelle für Langsamdreher stellen den Schwerpunkt dieser Arbeit dar. In diesem Zusammenhang wird ein Konzept zur Evaluation der Prognosegüte sporadischer Zeitreihen im multivariaten Fall aufgestellt, in welchem zum einen die Auswahl geeigneter multivariater Modelle erfolgt. Zum anderen wird eine Methodik zur Gruppierung von Langsamdreherzeitreihen mit gemeinsamen zeitlichen Verlaufsstrukturen zum Zweck der Prognose beschrieben. Im Rahmen einer Prognoseevaluation wird die Güte multivariater und konkurrierender univariater Prognoseverfahren sowohl statistisch als auch kostenorientiert in einem einfachen Lagerhaltungssystem bewertet. Die Ergebnisse der Untersuchung (sowohl die Erkenntnisse der empirischern Untersuchung als auch die Beschreibung einzelner Algorithmen und Prognoseverfahren) stellen einen Mehrwert für Unternehmen der Güterwirtschaft dar, in welchen der Großteil des Sortiments durch Langsamdreherprodukte vertreten ist.

Data Mining and Learning Analytics

Applications in Educational Research
Author: Samira ElAtia,Osmar R. Za?ane,Donald Ipperciel
Publisher: John Wiley & Sons
ISBN: 1118998235
Category: Computers
Page: 320
View: 584

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Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining’s four guiding principles— prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDM’s emerging role in helping to advance educational research—from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields. Includes case studies where data mining techniques have been effectively applied to advance teaching and learning Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research.

Applied Predictive Analytics

Principles and Techniques for the Professional Data Analyst
Author: Dean Abbott
Publisher: John Wiley & Sons
ISBN: 111872769X
Category: Computers
Page: 456
View: 761

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Learn the art and science of predictive analytics —techniques that get results Predictive analytics is what translates big data intomeaningful, usable business information. Written by a leadingexpert in the field, this guide examines the science of theunderlying algorithms as well as the principles and best practicesthat govern the art of predictive analytics. It clearly explainsthe theory behind predictive analytics, teaches the methods,principles, and techniques for conducting predictive analyticsprojects, and offers tips and tricks that are essential forsuccessful predictive modeling. Hands-on examples and case studiesare included. The ability to successfully apply predictive analytics enablesbusinesses to effectively interpret big data; essential forcompetition today This guide teaches not only the principles of predictiveanalytics, but also how to apply them to achieve real, pragmaticsolutions Explains methods, principles, and techniques for conductingpredictive analytics projects from start to finish Illustrates each technique with hands-on examples and includesas series of in-depth case studies that apply predictive analyticsto common business scenarios A companion website provides all the data sets used to generatethe examples as well as a free trial version of software Applied Predictive Analytics arms data and businessanalysts and business managers with the tools they need tointerpret and capitalize on big data.

Applied Data Mining for Business and Industry


Author: Paolo Giudici,Silvia Figini
Publisher: John Wiley & Sons
ISBN: 0470058862
Category: Computers
Page: 249
View: 2747

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This new edition sees the inclusion of 70% new material, including eight new case studies, that brings this best selling title up to date with the many advances made in the field since its original publication. In the text all the methods described are either computational or of a statistical modelling nature; complex probabilistic models and mathematical tools are not used, so the book is accessible to a wide audience of both students and industry professionals.

Data Mining for Business Analytics

Concepts, Techniques, and Applications with JMP Pro
Author: Galit Shmueli,Peter C. Bruce,Mia L. Stephens,Nitin R. Patel
Publisher: John Wiley & Sons
ISBN: 1118956621
Category: Mathematics
Page: 464
View: 3583

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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 Mining and Statistics for Decision Making


Author: Stéphane Tufféry
Publisher: John Wiley & Sons
ISBN: 9780470979280
Category: Computers
Page: 716
View: 4585

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Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives. This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized linear models, regularized regression, PLS regression, decision trees, neural networks, support vector machines, Vapnik theory, naive Bayesian classifier, ensemble learning and detection of association rules. They are discussed along with illustrative examples throughout the book to explain the theory of these methods, as well as their strengths and limitations. Key Features: Presents a comprehensive introduction to all techniques used in data mining and statistical learning, from classical to latest techniques. Starts from basic principles up to advanced concepts. Includes many step-by-step examples with the main software (R, SAS, IBM SPSS) as well as a thorough discussion and comparison of those software. Gives practical tips for data mining implementation to solve real world problems. Looks at a range of tools and applications, such as association rules, web mining and text mining, with a special focus on credit scoring. Supported by an accompanying website hosting datasets and user analysis. Statisticians and business intelligence analysts, students as well as computer science, biology, marketing and financial risk professionals in both commercial and government organizations across all business and industry sectors will benefit from this book.

Data Mining for Business Intelligence

Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner
Author: Galit Shmueli,Nitin R. Patel,Peter C. Bruce
Publisher: John Wiley and Sons
ISBN: 1118126041
Category: Mathematics
Page: 428
View: 2727

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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.

Pharmaceutical Data Mining

Approaches and Applications for Drug Discovery
Author: Konstantin V. Balakin
Publisher: John Wiley & Sons
ISBN: 9780470567616
Category: Medical
Page: 584
View: 4532

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Leading experts illustrate how sophisticated computational data mining techniques can impact contemporary drug discovery and development In the era of post-genomic drug development, extracting and applying knowledge from chemical, biological, and clinical data is one of the greatest challenges facing the pharmaceutical industry. Pharmaceutical Data Mining brings together contributions from leading academic and industrial scientists, who address both the implementation of new data mining technologies and application issues in the industry. This accessible, comprehensive collection discusses important theoretical and practical aspects of pharmaceutical data mining, focusing on diverse approaches for drug discovery—including chemogenomics, toxicogenomics, and individual drug response prediction. The five main sections of this volume cover: A general overview of the discipline, from its foundations to contemporary industrial applications Chemoinformatics-based applications Bioinformatics-based applications Data mining methods in clinical development Data mining algorithms, technologies, and software tools, with emphasis on advanced algorithms and software that are currently used in the industry or represent promising approaches In one concentrated reference, Pharmaceutical Data Mining reveals the role and possibilities of these sophisticated techniques in contemporary drug discovery and development. It is ideal for graduate-level courses covering pharmaceutical science, computational chemistry, and bioinformatics. In addition, it provides insight to pharmaceutical scientists, principal investigators, principal scientists, research directors, and all scientists working in the field of drug discovery and development and associated industries.

R in a Nutshell


Author: Joseph Adler
Publisher: O'Reilly Germany
ISBN: 3897216507
Category: Computers
Page: 768
View: 7195

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Wozu sollte man R lernen? Da gibt es viele Gründe: Weil man damit natürlich ganz andere Möglichkeiten hat als mit einer Tabellenkalkulation wie Excel, aber auch mehr Spielraum als mit gängiger Statistiksoftware wie SPSS und SAS. Anders als bei diesen Programmen hat man nämlich direkten Zugriff auf dieselbe, vollwertige Programmiersprache, mit der die fertigen Analyse- und Visualisierungsmethoden realisiert sind – so lassen sich nahtlos eigene Algorithmen integrieren und komplexe Arbeitsabläufe realisieren. Und nicht zuletzt, weil R offen gegenüber beliebigen Datenquellen ist, von der einfachen Textdatei über binäre Fremdformate bis hin zu den ganz großen relationalen Datenbanken. Zudem ist R Open Source und erobert momentan von der universitären Welt aus die professionelle Statistik. R kann viel. Und Sie können viel mit R machen – wenn Sie wissen, wie es geht. Willkommen in der R-Welt: Installieren Sie R und stöbern Sie in Ihrem gut bestückten Werkzeugkasten: Sie haben eine Konsole und eine grafische Benutzeroberfläche, unzählige vordefinierte Analyse- und Visualisierungsoperationen – und Pakete, Pakete, Pakete. Für quasi jeden statistischen Anwendungsbereich können Sie sich aus dem reichen Schatz der R-Community bedienen. Sprechen Sie R! Sie müssen Syntax und Grammatik von R nicht lernen – wie im Auslandsurlaub kommen Sie auch hier gut mit ein paar aufgeschnappten Brocken aus. Aber es lohnt sich: Wenn Sie wissen, was es mit R-Objekten auf sich hat, wie Sie eigene Funktionen schreiben und Ihre eigenen Pakete schnüren, sind Sie bei der Analyse Ihrer Daten noch flexibler und effektiver. Datenanalyse und Statistik in der Praxis: Anhand unzähliger Beispiele aus Medizin, Wirtschaft, Sport und Bioinformatik lernen Sie, wie Sie Daten aufbereiten, mithilfe der Grafikfunktionen des lattice-Pakets darstellen, statistische Tests durchführen und Modelle anpassen. Danach werden Ihnen Ihre Daten nichts mehr verheimlichen.

Making Sense of Data

A Practical Guide to Exploratory Data Analysis and Data Mining
Author: Glenn J. Myatt
Publisher: John Wiley & Sons
ISBN: 0470101016
Category: Mathematics
Page: 288
View: 539

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Data Mining and Machine Learning in Building Energy Analysis

Towards High Performance Computing
Author: Frédéric Magoules,Hai-Xiang Zhao
Publisher: John Wiley & Sons
ISBN: 1848214227
Category: Computers
Page: 186
View: 1502

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Focusing on up-to-date artificial intelligence models to solve building energy problems, Artificial Intelligence for Building Energy Analysis reviews recently developed models for solving these issues, including detailed and simplified engineering methods, statistical methods, and artificial intelligence methods. The text also simulates energy consumption profiles for single and multiple buildings. Based on these datasets, Support Vector Machine (SVM) models are trained and tested to do the prediction. Suitable for novice, intermediate, and advanced readers, this is a vital resource for building designers, engineers, and students.

Making Sense of Data II

A Practical Guide to Data Visualization, Advanced Data Mining Methods, and Applications
Author: Glenn J. Myatt,Wayne P. Johnson
Publisher: John Wiley & Sons
ISBN: 9780470417393
Category: Mathematics
Page: 308
View: 3218

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A hands-on guide to making valuable decisions from data using advanced data mining methods and techniques This second installment in the Making Sense of Data series continues to explore a diverse range of commonly used approaches to making and communicating decisions from data. Delving into more technical topics, this book equips readers with advanced data mining methods that are needed to successfully translate raw data into smart decisions across various fields of research including business, engineering, finance, and the social sciences. Following a comprehensive introduction that details how to define a problem, perform an analysis, and deploy the results, Making Sense of Data II addresses the following key techniques for advanced data analysis: Data Visualization reviews principles and methods for understanding and communicating data through the use of visualization including single variables, the relationship between two or more variables, groupings in data, and dynamic approaches to interacting with data through graphical user interfaces. Clustering outlines common approaches to clustering data sets and provides detailed explanations of methods for determining the distance between observations and procedures for clustering observations. Agglomerative hierarchical clustering, partitioned-based clustering, and fuzzy clustering are also discussed. Predictive Analytics presents a discussion on how to build and assess models, along with a series of predictive analytics that can be used in a variety of situations including principal component analysis, multiple linear regression, discriminate analysis, logistic regression, and Naïve Bayes. Applications demonstrates the current uses of data mining across a wide range of industries and features case studies that illustrate the related applications in real-world scenarios. Each method is discussed within the context of a data mining process including defining the problem and deploying the results, and readers are provided with guidance on when and how each method should be used. The related Web site for the series (www.makingsenseofdata.com) provides a hands-on data analysis and data mining experience. Readers wishing to gain more practical experience will benefit from the tutorial section of the book in conjunction with the TraceisTM software, which is freely available online. With its comprehensive collection of advanced data mining methods coupled with tutorials for applications in a range of fields, Making Sense of Data II is an indispensable book for courses on data analysis and data mining at the upper-undergraduate and graduate levels. It also serves as a valuable reference for researchers and professionals who are interested in learning how to accomplish effective decision making from data and understanding if data analysis and data mining methods could help their organization.

Knowledge Discovery in Bioinformatics

Techniques, Methods, and Applications
Author: Xiaohua Hu,Yi Pan
Publisher: John Wiley & Sons
ISBN: 9780470124635
Category: Technology & Engineering
Page: 400
View: 7799

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The purpose of this edited book is to bring together the ideas and findings of data mining researchers and bioinformaticians by discussing cutting-edge research topics such as, gene expressions, protein/RNA structure prediction, phylogenetics, sequence and structural motifs, genomics and proteomics, gene findings, drug design, RNAi and microRNA analysis, text mining in bioinformatics, modelling of biochemical pathways, biomedical ontologies, system biology and pathways, and biological database management.

big data @ work

Chancen erkennen, Risiken verstehen
Author: Thomas H. Davenport
Publisher: Vahlen
ISBN: 3800648156
Category: Fiction
Page: 214
View: 1630

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Big Data in Unternehmen. Dieses neue Buch gibt Managern ein umfassendes Verständnis dafür, welche Bedeutung Big Data für Unternehmen zukünftig haben wird und wie Big Data tatsächlich genutzt werden kann. Am Ende jedes Kapitels aktivieren Fragen, selbst nach Lösungen für eine erfolgreiche Implementierung und Nutzung von Big Data im eigenen Unternehmen zu suchen. Die Schwerpunkte - Warum Big Data für Sie und Ihr Unternehmen wichtig ist - Wie Big Data Ihre Arbeit, Ihr Unternehmen und Ihre Branche verändern - - wird - Entwicklung einer Big Data-Strategie - Der menschliche Aspekt von Big Data - Technologien für Big Data - Wie Sie erfolgreich mit Big Data arbeiten - Was Sie von Start-ups und Online-Unternehmen lernen können - Was Sie von großen Unternehmen lernen können: Big Data und Analytics 3.0 Der Experte Thomas H. Davenport ist Professor für Informationstechnologie und -management am Babson College und Forschungswissenschaftler am MIT Center for Digital Business. Zudem ist er Mitbegründer und Forschungsdirektor am International Institute for Analytics und Senior Berater von Deloitte Analytics.