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Giudici Paolo Applied Data Mining for Business and Industry


The increasing availability of data in our current, information overloaded society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract knowledge from such data. This book provides an accessible introduction to data mining methods in a consistent and application oriented statistical framework, using case studies drawn from real industry projects and highlighting the use of data mining methods in a variety of business applications. Introduces data mining methods and applications. Covers classical and Bayesian multivariate statistical methodology as well as machine learning and computational data mining methods. Includes many recent developments such as association and sequence rules, graphical Markov models, lifetime value modelling, credit risk, operational risk and web mining. Features detailed case studies based on applied projects within industry. Incorporates discussion of data mining software, with case studies analysed using R. Is accessible to anyone with a basic knowledge of statistics or data analysis. Includes an extensive bibliography and pointers to further reading within the text. Applied Data Mining for Business and Industry, 2nd edition is aimed at advanced undergraduate and graduate students of data mining, applied statistics, database management, computer science and economics. The case studies will provide guidance to professionals working in industry on projects involving large volumes of data, such as customer relationship management, web design, risk management, marketing, economics and finance.

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Tim Rey Applied Data Mining for Forecasting Using SAS


Applied Data Mining for Forecasting Using SAS, by Tim Rey, Arthur Kordon, and Chip Wells, introduces and describes approaches for mining large time series data sets. Written for forecasting practitioners, engineers, statisticians, and economists, the book details how to select useful candidate input variables for time series regression models in environments when the number of candidates is large, and identifies the correlation structure between selected candidate inputs and the forecast variable.

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Tsiptsis Konstantinos K. Data Mining Techniques in CRM. Inside Customer Segmentation


This is an applied handbook for the application of data mining techniques in the CRM framework. It combines a technical and a business perspective to cover the needs of business users who are looking for a practical guide on data mining. It focuses on Customer Segmentation and presents guidelines for the development of actionable segmentation schemes. By using non-technical language it guides readers through all the phases of the data mining process.

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Группа авторов Applied Data Mining


Data mining can be defined as the process of selection, exploration and modelling of large databases, in order to discover models and patterns. The increasing availability of data in the current information society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract such knowledge from data. Applications occur in many different fields, including statistics, computer science, machine learning, economics, marketing and finance. This book is the first to describe applied data mining methods in a consistent statistical framework, and then show how they can be applied in practice. 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 students and industry professionals. The second half of the book consists of nine case studies, taken from the author's own work in industry, that demonstrate how the methods described can be applied to real problems. Provides a solid introduction to applied data mining methods in a consistent statistical framework Includes coverage of classical, multivariate and Bayesian statistical methodology Includes many recent developments such as web mining, sequential Bayesian analysis and memory based reasoning Each statistical method described is illustrated with real life applications Features a number of detailed case studies based on applied projects within industry Incorporates discussion on software used in data mining, with particular emphasis on SAS Supported by a website featuring data sets, software and additional material Includes an extensive bibliography and pointers to further reading within the text Author has many years experience teaching introductory and multivariate statistics and data mining, and working on applied projects within industry A valuable resource for advanced undergraduate and graduate students of applied statistics, data mining, computer science and economics, as well as for professionals working in industry on projects involving large volumes of data – such as in marketing or financial risk management.

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Antonios Chorianopoulos Effective CRM using Predictive Analytics


A step-by-step guide to data mining applications in CRM. Following a handbook approach, this book bridges the gap between analytics and their use in everyday marketing, providing guidance on solving real business problems using data mining techniques. The book is organized into three parts. Part one provides a methodological roadmap, covering both the business and the technical aspects. The data mining process is presented in detail along with specific guidelines for the development of optimized acquisition, cross/ deep/ up selling and retention campaigns, as well as effective customer segmentation schemes. In part two, some of the most useful data mining algorithms are explained in a simple and comprehensive way for business users with no technical expertise. Part three is packed with real world case studies which employ the use of three leading data mining tools: IBM SPSS Modeler, RapidMiner and Data Mining for Excel. Case studies from industries including banking, retail and telecommunications are presented in detail so as to serve as templates for developing similar applications. Key Features: Includes numerous real-world case studies which are presented step by step, demystifying the usage of data mining models and clarifying all the methodological issues. Topics are presented with the use of three leading data mining tools: IBM SPSS Modeler, RapidMiner and Data Mining for Excel. Accompanied by a website featuring material from each case study, including datasets and relevant code. Combining data mining and business knowledge, this practical book provides all the necessary information for designing, setting up, executing and deploying data mining techniques in CRM. Effective CRM using Predictive Analytics will benefit data mining practitioners and consultants, data analysts, statisticians, and CRM officers. The book will also be useful to academics and students interested in applied data mining.

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Gordon Linoff S. Data Mining Techniques. For Marketing, Sales, and Customer Relationship Management


Packed with more than forty percent new and updated material, this edition shows business managers, marketing analysts, and data mining specialists how to harness fundamental data mining methods and techniques to solve common types of business problems Each chapter covers a new data mining technique, and then shows readers how to apply the technique for improved marketing, sales, and customer support The authors build on their reputation for concise, clear, and practical explanations of complex concepts, making this book the perfect introduction to data mining More advanced chapters cover such topics as how to prepare data for analysis and how to create the necessary infrastructure for data mining Covers core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, clustering, and survival analysis

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Ahlemeyer-Stubbe Andrea A Practical Guide to Data Mining for Business and Industry


Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is complemented by case studies to create a versatile reference book, allowing readers to look for specific methods as well as for specific applications. The book is formatted to allow statisticians, computer scientists, and economists to cross-reference from a particular application or method to sectors of interest.

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Sushmita Mitra Data Mining


First title to ever present soft computing approaches and their application in data mining, along with the traditional hard-computing approaches Addresses the principles of multimedia data compression techniques (for image, video, text) and their role in data mining Discusses principles and classical algorithms on string matching and their role in data mining

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Mourad Elloumi Biological Knowledge Discovery Handbook. Preprocessing, Mining and Postprocessing of Biological Data


The first comprehensive overview of preprocessing, mining, and postprocessing of biological data Molecular biology is undergoing exponential growth in both the volume and complexity of biological data—and knowledge discovery offers the capacity to automate complex search and data analysis tasks. This book presents a vast overview of the most recent developments on techniques and approaches in the field of biological knowledge discovery and data mining (KDD)—providing in-depth fundamental and technical field information on the most important topics encountered. Written by top experts, Biological Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data covers the three main phases of knowledge discovery (data preprocessing, data processing—also known as data mining—and data postprocessing) and analyzes both verification systems and discovery systems. BIOLOGICAL DATA PREPROCESSING Part A: Biological Data Management Part B: Biological Data Modeling Part C: Biological Feature Extraction Part D Biological Feature Selection BIOLOGICAL DATA MINING Part E: Regression Analysis of Biological Data Part F Biological Data Clustering Part G: Biological Data Classification Part H: Association Rules Learning from Biological Data Part I: Text Mining and Application to Biological Data Part J: High-Performance Computing for Biological Data Mining Combining sound theory with practical applications in molecular biology, Biological Knowledge Discovery Handbook is ideal for courses in bioinformatics and biological KDD as well as for practitioners and professional researchers in computer science, life science, and mathematics.

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Gordon Linoff S. Data Mining Techniques. For Marketing, Sales, and Customer Relationship Management


The leading introductory book on data mining, fully updated and revised! When Berry and Linoff wrote the first edition of Data Mining Techniques in the late 1990s, data mining was just starting to move out of the lab and into the office and has since grown to become an indispensable tool of modern business. This new edition—more than 50% new and revised— is a significant update from the previous one, and shows you how to harness the newest data mining methods and techniques to solve common business problems. The duo of unparalleled authors share invaluable advice for improving response rates to direct marketing campaigns, identifying new customer segments, and estimating credit risk. In addition, they cover more advanced topics such as preparing data for analysis and creating the necessary infrastructure for data mining at your company. Features significant updates since the previous edition and updates you on best practices for using data mining methods and techniques for solving common business problems Covers a new data mining technique in every chapter along with clear, concise explanations on how to apply each technique immediately Touches on core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, survival analysis, and more Provides best practices for performing data mining using simple tools such as Excel Data Mining Techniques, Third Edition covers a new data mining technique with each successive chapter and then demonstrates how you can apply that technique for improved marketing, sales, and customer support to get immediate results.

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Giudici, P: Applied Data Mining for Business and Industry ...

Giudici, P: Applied Data Mining for Business and Industry (Statistics in Practice) | Giudici, Paolo, Figini, Silvia | ISBN: 9780470058879 | Kostenloser Versand für alle Bücher mit Versand und Verkauf duch Amazon.

Applied Data Mining: Statistical Methods for Business and ...

Applied Data Mining: Statistical Methods for Business and Industry (Statistics in Practice) | Giudici, Paolo | ISBN: 9780470846780 | Kostenloser Versand für alle Bücher mit Versand und Verkauf duch Amazon.

Applied Data Mining: Statistical Methods for Business and ...

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APPLIED DATA MINING PAOLO GIUDICI PDF

Applied Data Mining for Business and Industry : Paolo Giudici : Goodreads is the world’s largest site for readers with over 50 million reviews. Covers classical and Bayesian multivariate statistical methodology as well as machine learning and computational data mining methods.

Applied Data Mining: Statistical Methods for Business and ...

Applied Data Mining: Statistical Methods for Business and Industry (Statistics in Practice) - Kindle edition by Giudici, Paolo. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Applied Data Mining: Statistical Methods for Business and Industry (Statistics in Practice).

Applied data mining : statistical methods for business and ...

GIUDICI, Paolo. Applied data mining : statistical methods for business and industry.Chichester: Wiley, 2003. xii, 364. ISBN 0470846798. Další formáty: BibTeX LaTeX RIS

Applied Data Mining: Statistical Methods for Business and ...

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Applied Data Mining - index-of.co.uk

Giudici, Paolo. Applied data mining for business and industry / Paolo Giudici, Silvia Figini. – 2nd ed. p. cm. Includes bibliographical references and index. ISBN 978-0-470-05886-2 (cloth) – ISBN 978-0-470-05887-9 (pbk.) 1. Data mining. 2. Business–Data processing. 3. Commercial statistics. I. Figini, Silvia. II. Title. QA76.9.D343G75 2009 005.74068—dc22 2009008334 A catalogue record ...

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Paolo Giudici (Author) › Visit Amazon's Paolo Giudici Page. Find all the books, read about the author, and more. See search results for this author. Are you an author? Learn about Author Central. Paolo Giudici (Author) 3.1 out of 5 stars 5 ratings. ISBN-13: 978-0470846780. ISBN-10: 047084678X. Why is ISBN important? ISBN. This bar-code number lets you verify that you're getting exactly the ...

Applied Data Mining by Giudici, Paolo (ebook)

Data mining can be defined as the process of selection, exploration and modelling of large databases, in order to discover models and patterns. The increasing availability of data in the current information society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract such knowledge from data ...

Applied Data Mining - Giudici, Paolo - 9780470846780 | HPB

by Giudici, Paolo. Data mining can be defined as the process of selection, exploration and modelling of large databases, in order to discover models and patterns. The increasing availability of data in the current information society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract such ...

Applied Data Mining for Business and Industry: Amazon.de ...

A number of case studies are explored, taken from Giudici's own applied work in industry, that demonstrate how the methods described can be applied to real problems. The book is supported by website featuring data sets, software and additional material.

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Applied Data Mining: Statistical Methods for Business and ...

Applied Data Mining: Statistical Methods for Business and Industry: Giudici, Paolo: Amazon.com.mx: Libros ... por Paolo Giudici (Autor) 5.0 de 5 estrellas 1 calificación. Ver todos los formatos y ediciones Ocultar otros formatos y ediciones. Precio de Amazon Nuevo desde Usado desde Kindle "Vuelva a intentarlo" $3,556.80 — — Pasta dura "Vuelva a intentarlo" — $9,166.99 — Pasta blanda ...

giudici paolo - ZVAB

Paolo Giudici Statistical Models for Data Analysis [nach diesem Titel suchen] Springer International Publishing Jul 2013, 2013. ISBN: 9783319000312. Anbieter AHA-BUCH GmbH, (Einbeck, Deutschland) Bewertung: Anzahl: 2 In den Warenkorb Preis: EUR 181,89. Währung umrechnen . Versand: Gratis. Innerhalb Deutschland Versandziele, Kosten & Dauer. Anbieter- und Zahlungsinformationen Dieser Anbieter ...

PERSONAL INFORMATION Paolo Stefano Giudici Male | Date of ...

(2003, 83 citations) (Paolo Giudici) Applied data mining for business and industry. Wiley, London. Chinese translation, 2005. Second edition, 2009. 5. (2002, 53 citations) (Paolo Giudici, Gianluca Passerone) Data Mining of association structures to model consumer behaviour. Computational Statistics and data analysis, vol. 38, n.4, pp 533-541. 6. (2004, 52 citations) (Chiara Cornalba, Paolo ...

Giudici, Paolo | onAcademic

Giudici, Paolo Sarlin, Peter Spelta, A. Download Collect. Sovereign risk in the Euro area: a multivariate stochastic process approach. Giudici, Paolo Parisi, Laura Download Collect. CLADAG 2015 special issue: Selected papers on classification and data analysis ...

Applied Data Mining for Business and Industry By author ...

[(Applied Data Mining for Business and Industry)] [ By (author) Paolo Giudici, By (author) Silvia Figini ] [June, 2009] | Paolo Giudici | ISBN: | Kostenloser Versand für alle Bücher mit Versand und Verkauf duch Amazon.

CIS - Giudici, Paolo

Giudici, Paolo. Bayesian data mining, with application to benchmarking and credit scoring. Applied Stochastic Models in Business and Industry 2001. 17:69-81 Giudici, Paolo, Heckerman, David, Whittaker, Joe. Statistical models for data mining. Data Mining and Knowledge Discovery 2001. 5:163-165 Giudici, Paolo, Castelo, Robert.

‪Paolo Giudici‬ - ‪Google Scholar‬

Paolo Giudici. Professor of Statistics, University of Pavia. Verified email at unipv.it - Homepage. Fintech Financial Networks Financial risk management Graphical models Contagion. Articles Cited by Co-authors. Title . Sort. Sort by citations Sort by year Sort by title. Cited by. Cited by. Year; Applied Data Mining. P Giudici. 1129 * Efficient construction of reversible jump Markov chain Monte ...

Applied Data Mining for Business and Industry

Giudici, Paolo. Applied data mining for business and industry / Paolo Giudici, Silvia Figini. – 2nd ed. p. cm. Includes bibliographical references and index. ISBN 978-0-470-05886-2 (cloth ...

Applied Data Mining: Statistical Methods for Business and ...

Applied Data Mining book. Read reviews from world’s largest community for readers. Data mining can be defined as the process of selection, exploration an...

APPLIED DATA MINING PAOLO GIUDICI PDF - PDF Portor

: Applied Data Mining: Statistical Methods for Business and Industry (Statistics in Practice) (): Paolo Giudici: Books. Applied Data Mining for Business and Industry. Second Edition. PAOLO GIUDICI. Department of Economics, University of Pavia, Italy. SILVIA FIGINI. Faculty of.

Paolo Giudici / Free University of Bozen-Bolzano

Paolo Giudici is professor of business law. He is a graduate of Genoa Law School (1987, magna cum laude), an ECGI Research Associate and in the editorial board of “Le Società”, where he is in charge of capital markets law.

Applied Data Mining for Business and Industry : Paolo ...

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Paolo Giudici - Department of Economics and Quantitative Methods, University of Pavia, A lecturer in data mining, business statistics, data analysis and risk management, Professor Giudici is also the director of the data mining laboratory. He is the author of around 80 publications, and the coordinator of 2 national research grants on data mining, and local coordinator of a European integrated ...

Paolo Giudici - Professor of Statistics - Università degli ...

Visualizza il profilo di Paolo Giudici su LinkedIn, la più grande comunità professionale al mondo. Paolo ha indicato 12 esperienze lavorative sul suo profilo. Guarda il profilo completo su LinkedIn e scopri i collegamenti di Paolo e le offerte di lavoro presso aziende simili.

Paolo GIUDICI | Professor (Full) | Professor of Statistics ...

Paolo Giudici Data science can be defined as the interaction between computer programming, statistical learning, and one of the many possible domains where it can be applied.

Paolo GIUDICI | Università degli Studi di Modena e Reggio ...

Paolo GIUDICI of Università degli Studi di Modena e Reggio Emilia, Modena (UNIMO) | Read 193 publications | Contact Paolo GIUDICI

Paolo Emiliani Giudici – Wikipedia

Paolo Emiliani Giudici (* 13.Juni 1812 bei Mussomeli auf Sizilien; † 8. September 1872 in Tonbridge, England) war ein italienischer Literarhistoriker und Literaturwissenschaftler.. Leben. Nach dem Studium der Literaturwissenschaften erhielt er 1848 eine Professur an der Universität Pisa, verlor diese Stellung aber beim Greifen der politischen Reaktionsbewegung im Zuge der Restauration nach ...

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Giudici, Paolo (et al.) Vorschau Kapitel kaufen 26,70 € Traditional Balsamic Vinegar as Coded by Us. Seiten 85-109. Giudici, Paolo (et al.) Vorschau Kapitel kaufen 26,70 € The Ageing of Balsamics: Residence Time, Maturity, and Yield. Seiten 111-141. Giudici, Paolo (et al.) Vorschau Kapitel kaufen 26,70 € Sensorial Properties and Evaluation of Balsamic Vinegars. Seiten 143-162. Giudici ...

APPLIED DATA MINING PAOLO GIUDICI PDF - europein.eu

Applied Data Mining for Business and Industry : Paolo Giudici : The increasing availability of data in our current, informationoverloaded society has led to the need for valid tools for itsmodelling and analysis. Data mining and applied statistical methodsare the appropriate tools to extract knowledge from such data. Applied Data Mining for ...

‪Paolo Pagnottoni‬ - ‪Google Scholar‬

‪University of Pavia‬ - ‪39-mal zitiert‬ - ‪Applied Statistics‬ - ‪Data Science‬ - ‪Financial Econometrics‬ - ‪Price Discovery‬ - ‪Cryptocurrency Market‬

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Paolo Giudici — RiskLab Finland

Paolo Giudici. Professor. Twitter Thumb-tack Linkedin Envelope. Biography. Paolo is Full Professor of Statistics at the Department of Economics and Management of the University of Pavia, Italy, and Board director of Credito Valtellinese Banking Group. He is a research fellow at the Bank of International Settlements (Basel), a research associate in big data analytics working groups at the ...

Paolo Giudici - Google Sites

Paolo Giudici. Professor of Statistics and Data Science at the Department of Economics and Management of the University of Pavia. Academic supervisor of about 180 Master's students and of 15 Phd students, currently working in the financial industry, in IT/consulting companies or as academic researchers. Author of several scientific publications (83 in Scopus, with 1180 total citations and an h ...

Author Page for Paolo Giudici :: SSRN

Applied Bayesian models, Graphical Gaussian Models, Systemic financial risk. 7. Market Risk, Connectedness and Turbulence: A Comparison of 21st Century Financial Crises . Number of pages: 30 Posted: 26 Apr 2020. Daniel Felix Ahelegbey and Paolo Giudici University of Pavia, Department of Economics and Management and University of Pavia Downloads 121 (249,594) View PDF; Download; Abstract ...

(PDF) Applied data mining: Statistical methods for ...

Data mining can be defined as the process of selection, exploration and modelling of large databases, in order to discover models and patterns. The increasing availability of data in the current information society has led to the need for valid tools

Paolo Giudici Profile | Facebook

Profile von Personen mit dem Namen Paolo Giudici anzeigen. Tritt Facebook bei, um dich mit Paolo Giudici und anderen Personen, die du kennen könntest, zu...

Applied data mining paolo giudici pdf 2003

(2003) (Paolo Giudici) Applied data mining: statistical methods for business and industry. Wiley, London. Chinese translation, 2005. Second edition (with Silvia Figini), 2009. Italian editions: Mc Graw Hill 2001, 2005. 5. (2002) (Paolo Giudici, Gianluca Passerone) Data Mining of association structures to model consumer behaviour. This book explores the concepts of data mining and data ...

Loop | Paolo Giudici

Professor of Statistics at the University of Pavia, where he teaches Statistics and Data Science (Department of Economics) and coordinates the Phd programme in Financial Technology (Department of Computer Engineering). Academic supervisor of about 160 Master's students and of 12 Phd students, currently working in the financial industry, in IT/consulting companies or as academic researchers ...

Wikizero - Paolo Emiliani Giudici

Paolo Emiliani Giudici (* 13.Juni 1812 bei Mussomeli auf Sizilien; † 8. September 1872 in Tonbridge, England) war ein italienischer Literarhistoriker und Literaturwissenschaftler.. Leben [Bearbeiten | Quelltext bearbeiten]. Nach dem Studium der Literaturwissenschaften erhielt er 1848 eine Professur an der Universität Pisa, verlor diese Stellung aber beim Greifen der politischen ...

Paolo Giudici / Freie Universität Bozen

Paolo Giudici is professor of business law. He is a graduate of Genoa Law School (1987, magna cum laude), an ECGI Research Associate and in the editorial board of “Le Società”, where he is in charge of capital markets law. He has published three books and a long list of articles and book chapters on Italian and European top law journals and books.<br /> Before starting his academic career ...

APPLIED DATA MINING PAOLO GIUDICI PDF - isrs2019.info

Applied Data Mining for Business and Industry. Would you like to change to the site? Paolo GiudiciSilvia Figini. Applied Giudicl Mining for Business and Industry, 2nd edition is aimed at advanced undergraduate and graduate students of data mining, applied statistics, database management, computer science and economics.

APPLIED DATA MINING PAOLO GIUDICI PDF - hools.mobi

Applied Data Mining for Business and Industry : Paolo Giudici : Thisbook provides an accessible introduction to data mining methods ina consistent and application oriented statistical framework, usingcase studies drawn from real industry projects and highlighting theuse of data mining methods in a variety of business applications.

Leben - db0nus869y26v.cloudfront.net

Paolo Emiliani Giudici (* 13.Juni 1812 bei Mussomeli auf Sizilien; † 8. September 1872 in Tonbridge, England) war ein italienischer Literarhistoriker und Literaturwissenschaftler.. Leben. Nach dem Studium der Literaturwissenschaften erhielt er 1848 eine Professur an der Universität Pisa, verlor diese Stellung aber beim Greifen der politischen Reaktionsbewegung im Zuge der Restauration nach ...

Pawel Cichosz Data Mining Algorithms. Explained Using R


Data Mining Algorithms is a practical, technically-oriented guide to data mining algorithms that covers the most important algorithms for building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality evaluation, and creating model ensembles. The author presents many of the important topics and methodologies widely used in data mining, whilst demonstrating the internal operation and usage of data mining algorithms using examples in R.

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Jamie MacLennan Data Mining with Microsoft SQL Server 2008


Understand how to use the new features of Microsoft SQL Server 2008 for data mining by using the tools in Data Mining with Microsoft SQL Server 2008, which will show you how to use the SQL Server Data Mining Toolset with Office 2007 to mine and analyze data. Explore each of the major data mining algorithms, including naive bayes, decision trees, time series, clustering, association rules, and neural networks. Learn more about topics like mining OLAP databases, data mining with SQL Server Integration Services 2008, and using Microsoft data mining to solve business analysis problems.

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Meta Brown S. Data Mining For Dummies


Delve into your data for the key to success Data mining is quickly becoming integral to creating value and business momentum. The ability to detect unseen patterns hidden in the numbers exhaustively generated by day-to-day operations allows savvy decision-makers to exploit every tool at their disposal in the pursuit of better business. By creating models and testing whether patterns hold up, it is possible to discover new intelligence that could change your business's entire paradigm for a more successful outcome. Data Mining for Dummies shows you why it doesn't take a data scientist to gain this advantage, and empowers average business people to start shaping a process relevant to their business's needs. In this book, you'll learn the hows and whys of mining to the depths of your data, and how to make the case for heavier investment into data mining capabilities. The book explains the details of the knowledge discovery process including: Model creation, validity testing, and interpretation Effective communication of findings Available tools, both paid and open-source Data selection, transformation, and evaluation Data Mining for Dummies takes you step-by-step through a real-world data-mining project using open-source tools that allow you to get immediate hands-on experience working with large amounts of data. You'll gain the confidence you need to start making data mining practices a routine part of your successful business. If you're serious about doing everything you can to push your company to the top, Data Mining for Dummies is your ticket to effective data mining.

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Группа авторов Data Mining Cookbook


Increase profits and reduce costs by utilizing this collection of models of the most commonly asked data mining questions In order to find new ways to improve customer sales and support, and as well as manage risk, business managers must be able to mine company databases. This book provides a step-by-step guide to creating and implementing models of the most commonly asked data mining questions. Readers will learn how to prepare data to mine, and develop accurate data mining questions. The author, who has over ten years of data mining experience, also provides actual tested models of specific data mining questions for marketing, sales, customer service and retention, and risk management. A CD-ROM, sold separately, provides these models for reader use.

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Walter Piegorsch W. Statistical Data Analytics. Foundations for Data Mining, Informatics, and Knowledge Discovery, Solutions Manual


Solutions Manual to accompany Statistical Data Analytics: Foundations for Data Mining, Informatics, and Knowledge Discovery A comprehensive introduction to statistical methods for data mining and knowledge discovery. Extensive solutions using actual data (with sample R programming code) are provided, illustrating diverse informatic sources in genomics, biomedicine, ecological remote sensing, astronomy, socioeconomics, marketing, advertising and finance, among many others.

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Paul Attewell Data Mining for the Social Sciences


We live in a world of big data: the amount of information collected on human behavior each day is staggering, and exponentially greater than at any time in the past. Additionally, powerful algorithms are capable of churning through seas of data to uncover patterns. Providing a simple and accessible introduction to data mining, Paul Attewell and David B. Monaghan discuss how data mining substantially differs from conventional statistical modeling familiar to most social scientists. The authors also empower social scientists to tap into these new resources and incorporate data mining methodologies in their analytical toolkits. Data Mining for the Social Sciences demystifies the process by describing the diverse set of techniques available, discussing the strengths and weaknesses of various approaches, and giving practical demonstrations of how to carry out analyses using tools in various statistical software packages.

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Russell Anderson K. Visual Data Mining. The VisMiner Approach


A visual approach to data mining. Data mining has been defined as the search for useful and previously unknown patterns in large datasets, yet when faced with the task of mining a large dataset, it is not always obvious where to start and how to proceed. This book introduces a visual methodology for data mining demonstrating the application of methodology along with a sequence of exercises using VisMiner. VisMiner has been developed by the author and provides a powerful visual data mining tool enabling the reader to see the data that they are working on and to visually evaluate the models created from the data. Key features: Presents visual support for all phases of data mining including dataset preparation. Provides a comprehensive set of non-trivial datasets and problems with accompanying software. Features 3-D visualizations of multi-dimensional datasets. Gives support for spatial data analysis with GIS like features. Describes data mining algorithms with guidance on when and how to use. Accompanied by VisMiner, a visual software tool for data mining, developed specifically to bridge the gap between theory and practice. Visual Data Mining: The VisMiner Approach is designed as a hands-on work book to introduce the methodologies to students in data mining, advanced statistics, and business intelligence courses. This book provides a set of tutorials, exercises, and case studies that support students in learning data mining processes. In praise of the VisMiner approach: «What we discovered among students was that the visualization concepts and tools brought the analysis alive in a way that was broadly understood and could be used to make sound decisions with greater certainty about the outcomes» —Dr. James V. Hansen, J. Owen Cherrington Professor, Marriott School, Brigham Young University, USA «Students learn best when they are able to visualize relationships between data and results during the data mining process. VisMiner is easy to learn and yet offers great visualization capabilities throughout the data mining process. My students liked it very much and so did I.» —Dr. Douglas Dean, Assoc. Professor of Information Systems, Marriott School, Brigham Young University, USA

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Samira ElAtia Data Mining and Learning Analytics. Applications in Educational Research


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.

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Ekins Sean Pharmaceutical Data Mining. Approaches and Applications for Drug Discovery


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.

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Galit Shmueli Data Mining for Business Analytics. Concepts, Techniques, and Applications with JMP Pro


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 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. 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, 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, including Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition, also published by Wiley. 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 and co-author of Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner ®, Third Edition, both published by Wiley. Mia Stephens is Academic Ambassador at JMP®, a division of SAS Institute. Prior to joining SAS, she was an adjunct professor of statistics at the University of New Hampshire and a founding member of the North Haven Group LLC, a statistical training and consulting company. She is the co-author of three other books, including Visual Six Sigma: Making Data Analysis Lean, Second Edition, 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. He is co-author of Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition, also published by Wiley.

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