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Author : Stéphane Tufféry
Publisher : Editions TECHNIP
ISBN 13 : 9782710808886
Total Pages : 564 pages
Book Rating : 4.8/5 (88 download)
Download or read book Data mining et statistique décisionnelle written by Stéphane Tufféry and published by Editions TECHNIP. This book was released on 2007 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Stéphane Tufféry
Publisher : Editions Technip
ISBN 13 : 9782710811800
Total Pages : 914 pages
Book Rating : 4.8/5 (118 download)
Download or read book Data Mining et Statistique décisionnelle written by Stéphane Tufféry and published by Editions Technip. This book was released on 2017-10 with total page 914 pages. Available in PDF, EPUB and Kindle. Book excerpt: Le data mining et la data science sont de plus en plus répandus dans les entreprises et les organisations soucieuses d'extraire l'information pertinente de leurs bases de données, qu'elles peuvent utiliser pour expliquer et prévoir les phénomènes qui les concernent (risques, production, consommation, fidélisation...). Cette cinquième édition, actualisée et augmentée de 90 pages, fait le point sur le data mining, ses méthodes, ses outils et ses applications, qui vont du scoring jusqu'au text mining, objet d'un chapitre complètement remanié. Nombre de ses outils appartiennent à l'analyse des données et à la statistique "classiques" (analyse factorielle, classification automatique, analyse discriminante, régression logistique, modèles linéaires généralisés, régression pénalisée, régression clusterwise...) mais certains sont plus spécifiques au data mining, comme les réseaux de neurones, les algorithmes génétiques, les SVM, les arbres de décision, les forêts aléatoires, le boosting et la détection des règles d'associations. Les avancées les plus récentes du machine learning et les applications les plus actuelles des Big Data sont présentées, qui vont des algorithmes de reconnaissance d'image aux méthodes de plongement de mots en text mining. Les chapitres sur les réseaux de neurones et les SVM sont illustrés par la reconnaissance de l'écriture manuscrite. Ces outils sont disponibles dans des logiciels de plus en plus puissants et complets, à commencer par le logiciel libre R, que nous comparons en détail aux logiciels SAS et IBM SPSS dans un chapitre spécifique. Ces logiciels sont utilisés pour illustrer par des exemples précis les explications théoriques données. Les aspects méthodologiques vont de la conduite des projets jusqu'aux facteurs de réussite et aux pièges à éviter, en passant par l'évaluation et la comparaison des modèles, leur intégration dans les processus opérationnels. Un chapitre est consacré à une étude de cas complète de credit scoring, de l'exploration des données jusqu'à l'élaboration de la grille de score.
Author : Stéphane Tufféry
Publisher : John Wiley & Sons
ISBN 13 : 0470979283
Total Pages : 748 pages
Book Rating : 4.4/5 (79 download)
Download or read book Data Mining and Statistics for Decision Making written by Stéphane Tufféry and published by John Wiley & Sons. This book was released on 2011-03-23 with total page 748 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Stéphane Tufféry
Publisher : Editions TECHNIP
ISBN 13 : 9782710809364
Total Pages : 386 pages
Book Rating : 4.8/5 (93 download)
Download or read book Etude de cas en statistique décisionnelle written by Stéphane Tufféry and published by Editions TECHNIP. This book was released on 2009 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprendre les principes théoriques de la statistique est une chose ; savoir les mettre en pratique en est une autre, et le fossé peut être large entre les deux. C'est pour vous aider à le franchir que l'auteur a écrit un ouvrage de " travaux pratiques " de la statistique décisionnelle, qui fait suite à son ouvrage de cours, Data Ming et statistique décisionnelle paru dans la même collection. Ce nouvel ouvrage présente une étude de cas réalisée de A à Z à partir du même jeu de données, et répondant de façon complète et cohérente à deux importantes problématiques de la statistique décisionnelle : la construction d'une segmentation de clientèle et l'élaboration d'un score d'appétence à l'achat d'un produit ou la souscription d'un contrat. Les données utilisées sont à la fois réelles et complètes. Elles proviennent du secteur de l'assurance, mais l'étude qu'elles permettent de réaliser peut se transposer à de nombreux autres domaines. Ces données sont disponibles sur Internez, notamment sur le site des Editions Technip où elles sont accompagnées des programmes présentés dans l'ouvrage. Ceci permettra au lecteur de compléter sa lecture par des exercices personnels, par le lest de variantes, mais aussi d'utiliser ces programmes pour les appliquer à ses propres données et ses propres projets. L'étude de cas est menée avec le logiciel SAS, qui est à la fois le plus complet et le plus répandu des logiciels commerciaux, et qui permet de traiter tous les sujets abordés dans l'ouvrage, et même d'optimiser, d'automatiser et d'industrialiser les traitements. Tout au long de l'ouvrage, une bonne partie des procédures classiques de SAS/STAT est passée en revue, mais, au-delà des questions de programmation, nous souhaitons surtout montrer au lecteur qu'il est souhaitable et possible de conjuguer rigueur et productivité. Pour le scoring, les trois principales méthodes de modélisation sont mises en oeuvre et comparées, l'analyse discriminante linéaire, les arbres de décision et la régression logistique, de même que l'agrégation de modèles par bagging.
Author : Kimito Funatsu
Publisher : BoD – Books on Demand
ISBN 13 : 9533075473
Total Pages : 600 pages
Book Rating : 4.5/5 (33 download)
Download or read book New Fundamental Technologies in Data Mining written by Kimito Funatsu and published by BoD – Books on Demand. This book was released on 2011-01-21 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining.
Author : Petra Perner
Publisher : Springer
ISBN 13 : 3642315372
Total Pages : 682 pages
Book Rating : 4.6/5 (423 download)
Download or read book Machine Learning and Data Mining in Pattern Recognition written by Petra Perner and published by Springer. This book was released on 2012-07-02 with total page 682 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 8th International Conference, MLDM 2012, held in Berlin, Germany in July 2012. The 51 revised full papers presented were carefully reviewed and selected from 212 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and web mining.
Author : Krzysztof J. Cios
Publisher : Springer Science & Business Media
ISBN 13 : 0387367950
Total Pages : 601 pages
Book Rating : 4.3/5 (873 download)
Download or read book Data Mining written by Krzysztof J. Cios and published by Springer Science & Business Media. This book was released on 2007-10-05 with total page 601 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribes the sequence in which data mining projects should be performed, from problem and data understanding through data preprocessing to deployment of the results. This knowledge discovery approach is what distinguishes Data Mining from other texts in this area. The book provides a suite of exercises and includes links to instructional presentations. Furthermore, it contains appendices of relevant mathematical material.
Author : Stéphane Tufféry
Publisher :
ISBN 13 : 9782710811879
Total Pages : 400 pages
Book Rating : 4.8/5 (118 download)
Download or read book Etude de cas en statistique décisionnelle written by Stéphane Tufféry and published by . This book was released on 2019-12-04 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Bruce Ratner
Publisher : CRC Press
ISBN 13 : 1466551216
Total Pages : 544 pages
Book Rating : 4.4/5 (665 download)
Download or read book Statistical and Machine-Learning Data Mining written by Bruce Ratner and published by CRC Press. This book was released on 2012-02-28 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. The first edition, titled Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data, contained 17 chapters of innovative and practical statistical data mining techniques. In this second edition, renamed to reflect the increased coverage of machine-learning data mining techniques, the author has completely revised, reorganized, and repositioned the original chapters and produced 14 new chapters of creative and useful machine-learning data mining techniques. In sum, the 31 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature. The statistical data mining methods effectively consider big data for identifying structures (variables) with the appropriate predictive power in order to yield reliable and robust large-scale statistical models and analyses. In contrast, the author's own GenIQ Model provides machine-learning solutions to common and virtually unapproachable statistical problems. GenIQ makes this possible — its utilitarian data mining features start where statistical data mining stops. This book contains essays offering detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. They address each methodology and assign its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.
Author : Chong Ho Alex Yu
Publisher : CRC Press
ISBN 13 : 1000777790
Total Pages : 290 pages
Book Rating : 4.0/5 (7 download)
Download or read book Data Mining and Exploration written by Chong Ho Alex Yu and published by CRC Press. This book was released on 2022-10-27 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces both conceptual and procedural aspects of cutting-edge data science methods, such as dynamic data visualization, artificial neural networks, ensemble methods, and text mining. There are at least two unique elements that can set the book apart from its rivals. First, most students in social sciences, engineering, and business took at least one class in introductory statistics before learning data science. However, usually these courses do not discuss the similarities and differences between traditional statistics and modern data science; as a result learners are disoriented by this seemingly drastic paradigm shift. In reaction, some traditionalists reject data science altogether while some beginning data analysts employ data mining tools as a “black box”, without a comprehensive view of the foundational differences between traditional and modern methods (e.g., dichotomous thinking vs. pattern recognition, confirmation vs. exploration, single method vs. triangulation, single sample vs. cross-validation etc.). This book delineates the transition between classical methods and data science (e.g. from p value to Log Worth, from resampling to ensemble methods, from content analysis to text mining etc.). Second, this book aims to widen the learner's horizon by covering a plethora of software tools. When a technician has a hammer, every problem seems to be a nail. By the same token, many textbooks focus on a single software package only, and consequently the learner tends to fit the problem with the tool, but not the other way around. To rectify the situation, a competent analyst should be equipped with a tool set, rather than a single tool. For example, when the analyst works with crucial data in a highly regulated industry, such as pharmaceutical and banking, commercial software modules (e.g., SAS) are indispensable. For a mid-size and small company, open-source packages such as Python would come in handy. If the research goal is to create an executive summary quickly, the logical choice is rapid model comparison. If the analyst would like to explore the data by asking what-if questions, then dynamic graphing in JMP Pro is a better option. This book uses concrete examples to explain the pros and cons of various software applications.
Author : Paolo Giudici
Publisher : John Wiley & Sons
ISBN 13 : 0470058862
Total Pages : 277 pages
Book Rating : 4.4/5 (7 download)
Download or read book Applied Data Mining for Business and Industry written by Paolo Giudici and published by John Wiley & Sons. This book was released on 2009-05-26 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Paolo Giudici
Publisher : John Wiley & Sons
ISBN 13 : 0470871393
Total Pages : 379 pages
Book Rating : 4.4/5 (78 download)
Download or read book Applied Data Mining written by Paolo Giudici and published by John Wiley & Sons. This book was released on 2005-09-27 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining can be defined as the process of selection, explorationand modelling of large databases, in order to discover models andpatterns. The increasing availability of data in the currentinformation society has led to the need for valid tools for itsmodelling and analysis. Data mining and applied statistical methodsare the appropriate tools to extract such knowledge from data.Applications occur in many different fields, including statistics,computer science, machine learning, economics, marketing andfinance. This book is the first to describe applied data mining methodsin a consistent statistical framework, and then show how they canbe applied in practice. All the methods described are eithercomputational, or of a statistical modelling nature. Complexprobabilistic models and mathematical tools are not used, so thebook is accessible to a wide audience of students and industryprofessionals. The second half of the book consists of nine casestudies, taken from the author's own work in industry, thatdemonstrate how the methods described can be applied to realproblems. Provides a solid introduction to applied data mining methods ina consistent statistical framework Includes coverage of classical, multivariate and Bayesianstatistical methodology Includes many recent developments such as web mining,sequential Bayesian analysis and memory based reasoning Each statistical method described is illustrated with real lifeapplications Features a number of detailed case studies based on appliedprojects within industry Incorporates discussion on software used in data mining, withparticular emphasis on SAS Supported by a website featuring data sets, software andadditional material Includes an extensive bibliography and pointers to furtherreading within the text Author has many years experience teaching introductory andmultivariate statistics and data mining, and working on appliedprojects within industry A valuable resource for advanced undergraduate and graduatestudents of applied statistics, data mining, computer science andeconomics, as well as for professionals working in industry onprojects involving large volumes of data - such as in marketing orfinancial risk management.
Author : BRASSEUR Christophe
Publisher : Lavoisier
ISBN 13 : 2746295202
Total Pages : 226 pages
Book Rating : 4.7/5 (462 download)
Download or read book Enjeux et usages du Big Data : Technologies, méthodes et mise en oeuvre written by BRASSEUR Christophe and published by Lavoisier. This book was released on 2013-04-01 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: Le développement spectaculaire d’internet, des réseaux sociaux, de la technologie mobile et la multiplication des capteurs provoquent une croissance exponentielle des données à laquelle les entreprises doivent faire face : c’est le phénomène du Big Data. Ses enjeux sont considérables. Au-delà de la simple question technique du stockage, il offre la possibilité de tirer profit du contenu de ces nouvelles sources d’information. Les solutions décisionnelles classiques laissent progressivement place au Business Analytics et aux méthodes prédictives, transformant l’avalanche de données en valeur ajoutée. La technologie est aujourd’hui disponible, les bases de données traditionnelles ont évolué et les solutions dédiées à l’exploitation des données massives, telles que Hadoop, sont désormais opérationnelles. S’appuyant sur différents cas pratiques, Enjeux et usages du Big Data met l’accent sur les méthodes, les techniques et les ressources nécessaires pour permettre aux entreprises d’entrer avec succès dans l’ère de l’information à grande échelle.
Author : Daniel T. Larose
Publisher : John Wiley & Sons
ISBN 13 : 0470908742
Total Pages : 336 pages
Book Rating : 4.4/5 (79 download)
Download or read book Discovering Knowledge in Data written by Daniel T. Larose and published by John Wiley & Sons. This book was released on 2014-07-08 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Author : Janusz Kacprzyk
Publisher : Springer Nature
ISBN 13 : 3031352483
Total Pages : 882 pages
Book Rating : 4.0/5 (313 download)
Download or read book International Conference on Advanced Intelligent Systems for Sustainable Development written by Janusz Kacprzyk and published by Springer Nature. This book was released on 2023-07-04 with total page 882 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the potential contributions of emerging technologies in different fields as well as the opportunities and challenges related to the integration of these technologies in the socio-economic sector. In this book, many latest technologies are addressed, particularly in the fields of computer science and engineering. The expected scientific papers covered state-of-the-art technologies, theoretical concepts, standards, product implementation, ongoing research projects, and innovative applications of Sustainable Development. This new technology highlights, the guiding principle of innovation for harnessing frontier technologies and taking full profit from the current technological revolution to reduce gaps that hold back truly inclusive and sustainable development. The fundamental and specific topics are Big Data Analytics, Wireless sensors, IoT, Geospatial technology, Engineering and Mechanization, Modeling Tools, Risk analytics, and preventive systems.
Author : Florin Gorunescu
Publisher : Springer Science & Business Media
ISBN 13 : 3642197213
Total Pages : 364 pages
Book Rating : 4.6/5 (421 download)
Download or read book Data Mining written by Florin Gorunescu and published by Springer Science & Business Media. This book was released on 2011-03-10 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: The knowledge discovery process is as old as Homo sapiens. Until some time ago this process was solely based on the ‘natural personal' computer provided by Mother Nature. Fortunately, in recent decades the problem has begun to be solved based on the development of the Data mining technology, aided by the huge computational power of the 'artificial' computers. Digging intelligently in different large databases, data mining aims to extract implicit, previously unknown and potentially useful information from data, since “knowledge is power”. The goal of this book is to provide, in a friendly way, both theoretical concepts and, especially, practical techniques of this exciting field, ready to be applied in real-world situations. Accordingly, it is meant for all those who wish to learn how to explore and analysis of large quantities of data in order to discover the hidden nugget of information.
Author : Jean-Louis Monino
Publisher : John Wiley & Sons
ISBN 13 : 1119779812
Total Pages : 224 pages
Book Rating : 4.1/5 (197 download)
Download or read book Data Control written by Jean-Louis Monino and published by John Wiley & Sons. This book was released on 2020-10-07 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: Businesses are becoming increasingly aware of the importance of data and information. As such, they are eager to develop ways to manage them, to enrich them and take advantage of them. Indeed, the recent explosion of a phenomenal amount of data, and the need to analyze it, brings to the forefront the well-known hierarchical model: Data, Information, Knowledge. Data? this new intangible manna ? is produced in real time. It arrives in a continuous stream and comes from a multitude of sources that are generally heterogeneous. This accumulation of data of all kinds is generating new activities designed to analyze these huge amounts of information. It is therefore necessary to adapt and try new approaches, methods, new knowledge and new ways of working. This leads to new properties and new issues as a logical reference must be created and implemented. At the company level, this mass of data is difficult to manage; interpreting it is the predominant challenge.
