 | SAS - Version 9.2 Introduction au décisionnel - Méthode et maîtrise du langage Commentaire Si, à sa création, le progiciel SAS était un logiciel de traitement statistique des données, il a depuis fortement évolué et est devenu le progiciel d'informatique décisionnelle (Business Intelligence) le plus utilisé. Le système SAS est très vaste et comprend de nombreuses applications. Il permet notamment la gestion et l’analyse des données. Ses utilisations varient selon les utilisateurs mais il existe un socle de connaissances communes que se propose d’explorer cet ouvrage. Il présente les connaissances fondamentales et transversales nécessaires à la programmation SAS, indispensable pour exploiter pleinement le logiciel. Il vise, au travers d’une exploration des fonctionnalités de Base SAS, à présenter la création des tables de données (manipulation, modification, gestion), et les procédures d’exploration des données (construction de tableaux, de rapports, etc.). Il explique comment générer des graphiques et produire des documents de présentation au format HTML, RTF ou PDF, et aborde le langage macro propre au système SAS. Les principales nouveautés de la version 9.2, mise sur le marché au cours du 2e semestre 2008, sont exposées. |
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 | Fouille de textes Méthodes, outils et applications de Fidelia Ibekwe-SanJuan Commentaire La fouille de textes est une thématique scientifique pluridisciplinaire mettant en relation la recherche universitaire, la recherche en milieu industriel, les préoccupations en sciences fondamentales et les enjeux politico-économiques. Elle fédère des thématiques issues des sciences de l'information, de la linguistique, de la statistique et des méthodes d'apprentissage (I.A). Dans un contexte où, grâce au Web, l'accès à l'information écrite est divers, abondant, multilingue et multihorizons, la fouille de textes est par excellence un domaine pour lequel il était nécessaire de faire un état des lieux. Cet ouvrage analyse en profondeur et de manière exhaustive les problématiques et les apports de la fouille de textes. Il est destiné aux doctorants, aux chercheurs et industriels souhaitant disposer d'une vision globale des méthodes et enjeux de ce nouveau paradigme de recherche. |
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 | Multimedia Data Mining and Knowledge Discovery de Valery A. Petrushin et Latifur Khan Commentaire Multimedia information is ubiquitous and essential in many applications, and repositories of multimedia are numerous and extremely large. Consequently, researchers and professionals need new techniques and tools for extracting the hidden, useful knowledge embedded within multimedia collections, thereby helping them discover relationships between the various elements and using this knowledge in decision-making applications. Multimedia Data Mining and Knowledge Discovery, assembling the work of leading academic and professional/industrial researchers worldwide, provides an overview of the current state-of-the-art in the field of multimedia data mining and knowledge discovery, and discusses the variety of hot topics in multimedia data mining research. Consisting of an introductory section and four topical parts, the book describes the objectives and current tendencies in multimedia data mining research and their applications. Each part contains an overview of its chapters and leads the reader with a structured approach through the diverse subjects in the field. Topics and Features: Features a comprehensive introduction to multimedia data mining and its relevance today Presents a global perspective of the field and its various components Provides broad, yet thorough and detailed coverage of the subject Numerous chapters reference websites with supplementary materials and demonstrations Explores multimedia data exploration, multimedia data modeling and evaluation, and visualization Offers an entire part devoted to applications and case studies Written with graduate students in mind, this much needed comprehensive survey of the current state of multimedia data mining and knowledge discovery will also serve as a valuable resource for researchers with interests in multimedia data mining, summarization, indexing, and retrieval. |
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 | Data Mining for Business Intelligence Concepts, Techniques, and Applications in Microsoft Office Excel® with XLMiner® Commentaire This book provides both a theoretical and practical understanding of the key methods of classification, prediction, reduction and exploration that are at the heart of data mining. In addition, it presents a business decision-making context for these methods and uses real business cases and data to illustrate the application and interpretation of these methods. |
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 | Java Data Mining Strategy, Standard, and Practice - A Practical Guide for architecture, design, and implementation de Mark F. Hornick , Erik Marcadé et Sunil Venkayala Commentaire "Java is now ubiquitous and over the past few years the Java world has shifted focus on--among other things--new frameworks, such as the Java Data Mining (JDM) framework. JDM addresses a clear need for standardization in data mining operations, yet to those approaching both Java and data mining the mountain seems as Everest. Hornick, Marcade, and Venkayala could not have written this book at a better time. To the expert it is reference and map of the landscape, and to the novice it will be a constant guide and companion to each journey in JDM. This book is approachable, usable, practical, and necessary for any Java data mining software architect, developer, or analyst." −Frank Byrum, Chief Scientist, CorMine Intelligent Data, LLC Whether you are a software developer, systems architect, data analyst, or business analyst, if you want to take advantage of data mining in the development of advanced analytic applications, Java Data Mining, JDM, the new standard now implemented in core DBMSs and data mining/analysis software, is a key solution component. And this book is the essential guide to the usage of the JDM standard interface. The reference that will help you produce applications with advanced analytics and predictive analytic capabilities. The first and authoritative guide to JDM, written by contributors to the JDM standard. The book discusses and illustrates how to solve real problems using the JDM API. The authors provide you with: • an overview of data mining and JDM’s place in strategic solutions to data mining-related problems; • JDM's essentials—the design approach and design issues, with detailed code examples; a Web Services interface to enable JDM functionality in an SOA environment; and illustration of JDM XML Schema for JDM objects; • JDM in practice—the use of JDM from vendor implementations and approaches to customer applications, integration, and usage; impact of data mining on IT infrastructure. Here, we illustrate how to build applications that use the JDM API. Mark F. Hornick is a senior manager of Data Mining Technologies at Oracle Corporation. He has lead the Java Data Mining (JSR-73) expert group since July of 2000, and now the JSR-247 expert group working towards JDM 2.0. Mark brings 20 years experience in the design and implementation of advanced software systems. Erik Marcade is Founder and Chief Technical Officer of KXEN, which provides next generation business analytics software, and a member of the JSR-73 and JSR-247 expert group. Sunil Venkayala, is a J2EE and XML group leader and a Principal Member of Technical Staff at Oracle Corporation. Sunil is also an expert group member of Java Data Mining (JDM) standard developed under JSR-73 and JSR-247. |
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 | Introduction to Data Mining International Edition de Pang Ning Tan, Michael Steinbach et Vipin Kumar Commentaire Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms. |
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 | Data Mining with SQL Server 2005 de Tang ZhaoHui et Jamie MacLennan Commentaire Your in-depth guide to using the new Microsoft(r) data mining standard to solve today's business problems Concealed inside your data warehouse and data marts is a wealth of valuable information just waiting to be discovered. All you need are the right tools to extract that information and put it to use. Serving as your expert guide, this book shows you how to create and implement data mining applications that will find the hidden patterns from your historical datasets. The authors explore the core concepts of data mining as well as the latest trends. They then reveal the best practices in the field, utilizing the innovative features of SQL Server 2005 so that you can begin building your own successful data mining projects. You'll learn: The principal concepts of data mining How to work with the data mining algorithms included in SQL Server data mining How to use DMX-the data mining query language The XML for Analysis API The architecture of the SQL Server 2005 data mining component How to extend the SQL Server 2005 data mining platform by plugging in your own algorithms How to implement a data mining project using SQL Server Integration Services How to mine an OLAP cube How to build an online retail site with cross-selling features How to access SQL Server 2005 data mining features programmatically |
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 | Clustering for Data mining a data recovery approach de Boris Mirkin Commentaire Often considered more as an art than a science, the field of clustering has been dominated by learning through examples and by choosing techniques almost through trial-and-error. Even the most popular clustering methods - K-Means for partitioning the data set and Ward's method for hierarchical clustering - have lacked the theoretical attention that would establish a firm relationship between the two methods and provide relevant interpretation aids. Rather than the traditional set of ad hoc techniques, Clustering for Data Mining: A Data Recovery Approach presents a theory that not only closes gaps in K-Means and Ward methods, but also extends the methods into areas of current interest, such as clustering mixed scale data and incomplete clustering. The author suggests original methods for both cluster finding and cluster description; addresses related topics such as principal component analysis, contingency measures, and data visualization; and includes nearly 60 computational examples covering all stages of clustering, from data preprocessing to cluster validation and results interpretation. This author's unique attention to data recovery methods, theory-based advice, pre- and post-processing issues and clear, practical instructions for real-world data mining make this book ideally suited for virtually all purposes: for teaching, for self-study, and for professional reference. Features Introduces classical clustering methods extended, via the data recovery approach, to modern data mining tasks Fills gaps in the established theory and corrects common misconceptions Treats the two most popular methods, K-Means and Ward clustering, offering the first theoretically motivated instructions for automating all steps of data mining with clustering Presents a wealth of computational examples covering all stages of clustering |
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 | Next generation of Data-Mining applications de Mehmed Kantardzic et Jozef Zurada Commentaire This book presents the next generation of data mining applications based on state-of-the art methodologies and techniques for analyzing enormous quantities of raw data in high-dimension Each chapter describes the data mining development process, results, and experiences with new data mining tools and techniques Includes twenty-five novel and diverse contributions from experienced and well-respected data mining scientists and practitioners that describe their recent applications using state-of-the-art methods and algorithms |
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 | Data Mining techniques de Michael J. . Berry et Gordon Linoff Commentaire * 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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