Search Results for "data-mining-and-predictive-analytics-wiley-series-on-methods-and-applications-in-data-mining"

Data Mining and Predictive Analytics

Data Mining and Predictive Analytics

  • Author: Daniel T. Larose,Chantal D. Larose
  • Publisher: John Wiley & Sons
  • ISBN: 1118868706
  • Category: Computers
  • Page: 824
  • View: 3467
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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, Second Edition: 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.com, with exclusive password-protected instructor content Data Mining and Predictive Analytics, Second Edition will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.

Discovering Knowledge in Data

Discovering Knowledge in Data

An Introduction to Data Mining

  • Author: Daniel T. Larose
  • Publisher: John Wiley & Sons
  • ISBN: 1118873572
  • Category: Computers
  • Page: 336
  • View: 4494
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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

Predictive Analytics für Dummies

Predictive Analytics für Dummies

  • Author: Anasse Bari,Mohamed Chaouchi,Tommy Jung
  • Publisher: John Wiley & Sons
  • ISBN: N.A
  • Category:
  • Page: 360
  • View: 1924
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Data Science für Unternehmen

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: 3920
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Multivariate Modellierung, Prognose und Evaluation sporadischer Nachfragezeitreihen

Multivariate Modellierung, Prognose und Evaluation sporadischer Nachfragezeitreihen

  • Author: Ekaterina Nieberle
  • Publisher: BoD – Books on Demand
  • ISBN: 3844104623
  • Category: Business & Economics
  • Page: 384
  • View: 5838
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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 for Business Analytics

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: 6564
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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 for Business Intelligence

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 & Sons
  • ISBN: 1118211391
  • Category: Mathematics
  • Page: 428
  • View: 6327
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Data Mining and Learning Analytics

Data Mining and Learning Analytics

Applications in Educational Research

  • Author: Samira ElAtia,Donald Ipperciel,Osmar R. Zaà ̄ane
  • Publisher: John Wiley & Sons
  • ISBN: 1118998219
  • Category: Computers
  • Page: 320
  • View: 2386
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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.

big data @ work

big data @ work

Chancen erkennen, Risiken verstehen

  • Author: Thomas H. Davenport
  • Publisher: Vahlen
  • ISBN: 3800648156
  • Category: Fiction
  • Page: 214
  • View: 4368
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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.

Predictive Analytics, Data Mining and Big Data

Predictive Analytics, Data Mining and Big Data

Myths, Misconceptions and Methods

  • Author: S. Finlay
  • Publisher: Springer
  • ISBN: 1137379286
  • Category: Business & Economics
  • Page: 260
  • View: 4298
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This in-depth guide provides managers with a solid understanding of data and data trends, the opportunities that it can offer to businesses, and the dangers of these technologies. Written in an accessible style, Steven Finlay provides a contextual roadmap for developing solutions that deliver benefits to organizations.

Applied Data Mining for Business and Industry

Applied Data Mining for Business and Industry

  • Author: Paolo Giudici,Silvia Figini
  • Publisher: John Wiley & Sons
  • ISBN: 0470058862
  • Category: Computers
  • Page: 249
  • View: 1004
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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 and Statistics for Decision Making

Data Mining and Statistics for Decision Making

  • Author: Stéphane Tufféry
  • Publisher: John Wiley & Sons
  • ISBN: 9780470979280
  • Category: Computers
  • Page: 716
  • View: 9367
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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.

R in a Nutshell

R in a Nutshell

  • Author: Joseph Adler
  • Publisher: O'Reilly Germany
  • ISBN: 3897216507
  • Category: Computers
  • Page: 768
  • View: 9516
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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.

Data mining

Data mining

praktische Werkzeuge und Techniken für das maschinelle Lernen

  • Author: Ian H. Witten,Eibe Frank
  • Publisher: N.A
  • ISBN: 9783446215337
  • Category:
  • Page: 386
  • View: 407
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Applied Predictive Analytics

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: 8766
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Learn the art and science of predictive analytics — techniques that get results Predictive analytics is what translates big data into meaningful, usable business information. Written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. It clearly explains the theory behind predictive analytics, teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful predictive modeling. Hands-on examples and case studies are included. The ability to successfully apply predictive analytics enables businesses to effectively interpret big data; essential for competition today This guide teaches not only the principles of predictive analytics, but also how to apply them to achieve real, pragmatic solutions Explains methods, principles, and techniques for conducting predictive analytics projects from start to finish Illustrates each technique with hands-on examples and includes as series of in-depth case studies that apply predictive analytics to common business scenarios A companion website provides all the data sets used to generate the examples as well as a free trial version of software Applied Predictive Analytics arms data and business analysts and business managers with the tools they need to interpret and capitalize on big data.

Making Sense of Data

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: 4050
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Methods and Applications of Statistics in Business, Finance, and Management Science

Methods and Applications of Statistics in Business, Finance, and Management Science

  • Author: N. Balakrishnan
  • Publisher: John Wiley & Sons
  • ISBN: 0470405104
  • Category: Mathematics
  • Page: 711
  • View: 8391
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Inspired by the Encyclopedia of Statistical Sciences, Second Edition (ESS2e), this volume presents a concise, well-rounded focus on the statistical concepts and applications that are essential for understanding gathered data in the study of business, finance, and management science. The book successfully upholds the goals of ESS2e by combining both previously-published and newly developed contributions written by over 100 leading academics, researchers, and practitioner in a comprehensive, approachable format. The result is a succinct reference that unveils modern, cutting-edge approaches to acquiring and analyzing data across diverse subject areas within these three disciplines, including risk management, mathematical finance, economics, supply chain management, derivative pricing, and resource allocation. In addition, techniques related to survey methodology, computational statistics, and operations research are discussed, where applicable. Topics of coverage include: Logistics Decision analysis Optimization Simulation Forecasting Mathematical modeling Data mining

Modeling Techniques in Predictive Analytics with Python and R

Modeling Techniques in Predictive Analytics with Python and R

A Guide to Data Science

  • Author: Thomas W. Miller
  • Publisher: FT Press
  • ISBN: 013389214X
  • Category: Computers
  • Page: 448
  • View: 8529
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Master predictive analytics, from start to finish Start with strategy and management Master methods and build models Transform your models into highly-effective code—in both Python and R This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math. Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work—and maximize their value. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code. If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more. All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you’re new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you’re already a modeler, programmer, or manager, you’ll learn crucial skills you don’t already have. Using Python and R, Miller addresses multiple business challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic code that delivers actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods. Use Python and R to gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more

Knowledge Discovery in Bioinformatics

Knowledge Discovery in Bioinformatics

Techniques, Methods, and Applications

  • Author: Xiaohua Hu,Yi Pan
  • Publisher: John Wiley & Sons
  • ISBN: 9780470124635
  • Category: Technology & Engineering
  • Page: 390
  • View: 2364
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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.

Real-Time Data Mining

Real-Time Data Mining

  • Author: Florian Stompe
  • Publisher: Diplomica Verlag
  • ISBN: 3836678799
  • Category: Business & Economics
  • Page: 106
  • View: 1014
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Data Mining ist ein inzwischen etabliertes, erfolgreiches Werkzeug zur Extraktion von neuem, bislang unbekanntem Wissen aus Daten. In mittlerweile fast allen gr eren Unternehmen wird es genutzt um Mehrwerte f r Kunden zu generieren, den Erfolg von Marketingkampagnen zu erh hen, Betrugsverdacht aufzudecken oder beispielsweise durch Segmentierung unterschiedliche Kundengruppen zu identifizieren. Ein Grundproblem der intelligenten Datenanalyse besteht darin, dass Daten oftmals in rasanter Geschwindigkeit neu entstehen. Eink ufe im Supermarkt, Telefonverbindungen oder der ffentliche Verkehr erzeugen t glich eine neue Flut an Daten, in denen potentiell wertvolles Wissen steckt. Die versteckten Zusammenh nge und Muster k nnen sich im Zeitverlauf mehr oder weniger stark ver ndern. Datenmodellierung findet in der Regel aber noch immer einmalig bzw. sporadisch auf dem Snapshot einer Datenbank statt. Einmal erkannte Muster oder Zusammenh nge werden auch dann noch angenommen, wenn diese l ngst nicht mehr bestehen. Gerade in dynamischen Umgebungen wie zum Beispiel einem Internet-Shop sind Data Mining Modelle daher schnell veraltet. Betrugsversuche k nnen dann unter Umst nden nicht mehr erkannt, Absatzpotentiale nicht mehr genutzt werden oder Produktempfehlungen basieren auf veralteten Warenk rben. Um dauerhaft Wettbewerbsvorteile erzielen zu k nnen, muss das Wissen ber Daten aber m glichst aktuell und von ausgezeichneter Qualit t sein. Der Inhalt dieses Buches skizziert Methoden und Vorgehensweisen von Data Mining in Echtzeit.