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Ralph Kimball, Margy Ross
Ralph Kimball invented a data warehousing technique called ?dimensional modelling? and popularised it in his first Wiley bestseller The Data Warehouse Toolkit. Since then dimensional modelling has become the most widely accepted technique for data warehouse design. Since the first edition, Kimball has improved on his earlier techniques and created many new ones. In this second edition, he provides a comprehensive collection of all of them, from basic to advanced, and strategies for optimising data warehouse design for common business applications. He includes examples for retail sales, inventory management, procurement, orders and invoices, customer relationship management, accounting, financial services, telecommunication and utilities, health care, insurance and more. He also presents unique modelling techniques for e-commerce and shows strategies for optimising performance. A companion Web site provides updates on dimensional modelling techniques, links to related sites and source code where appropriate.
Christopher D. Manning, Hinrich Schütze
An introduction to statistical natural language processing (NLP). The text contains the theory and algorithms needed for building NLP tools. Topics covered include: mathematical and linguistic foundations; statistical methods; collocation finding; word sense disambiguation; and probalistic parsing.
Martin Fowler, Rebecca Parsons
Martin Fowler's breakthrough practitioner-oriented book on Domain Specific Languages - will do for DSLs what Fowler did for refactoring! * *Fowler's highly anticipated introduction to DSLs: a category-defining book by one of the software world's most influential authors. *Two books in one: a concise narrative that introduces DSLs, and a larger reference that shows how to plan and develop them. *Helps software professionals reduce the cost and complexity of building DSLs - so they can take full advantage of them. Domain Specific Languages (DSLs) offer immense promise for software engineers who need better, faster ways to solve problems of specific types, or in specific areas or industries. DSLs have been around for several years, and have begun to grow in popularity. Now, Martin Fowler - one of the world's most influential software engineering authors - has written the first practitioner-oriented book about them. Fowler's legendary book, Refactoring, made software refactoring a crucial tool for software engineers worldwide; this book will do the same for DSLs. Fowler has designed Domain Specific Languages as two books in one. The first --a narrative designed to be read from 'cover to cover' - offers a concise introduction to DSLs, how they are implemented, and what are useful for. Next, Fowler thoroughly introduces today's most effective techniques for building DSLs. Fowler covers both 'external' and 'internal' DSLs, a well as alternative computational models, code generation, common parser topics, and much more. He provides extensive Java and C# examples throughout, as well as selected Ruby examples for concepts that can best be explained using a dynamic language. Together, both sections enable readers to make wellinformed choices about whether to use a DSL in their work, and which techniques to employ in order to build DSLs more quickly and cost-effectively.
Jamie MacLennan, ZhaoHui Tang, Bogdan Crivat
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.
Data warehouse and OLAP technology for data mining. Data preprocessing. Data mining primitives, languages, and system architecture. Concept description: characterization and comparison. Mining association rules in large databases. Classification and prediction. Cluster analysis. Mining complex types of data. Applications and trends in data mining. Appendix.
An invaluable resource for quantitative analysts who need to run models that assist in option pricing and risk management. This concise, practical hands on guide to Monte Carlo simulation introduces standard and advanced methods to the increasing complexity of derivatives portfolios. Ranging from pricing more complex derivatives, such as American and Asian options, to measuring Value at Risk, or modelling complex market dynamics, simulation is the only method general enough to capture the complexity and Monte Carlo simulation is the best pricing and risk management method available. The book is packed with numerous examples using real world data and is supplied with a CD to aid in the use of the examples.
Sam Anahory, Dennis Murray
This is a practical, hands-on guide which explains tried-and-true techniques for developing data warehouses using relational databases and open system technology. Written in "cookbook" format, this book covers all stages of implementation from project planning and requirements analysis, through architecture and design, to administrative issues such as user access, security, and back-up/recovery.
Gordon S. Linoff, Michael J. A. Berry
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.
Michael Berthold, David J. Hand
This second and revised edition contains a detailed introduction to the key classes of intelligent data analysis methods. The twelve coherently written chapters by leading experts provide complete coverage of the core issues. The first half of the book is devoted to the discussion of classical statistical issues. The following chapters concentrate on machine learning and artificial intelligence, rule induction methods, neural networks, fuzzy logic, and stochastic search methods. The book concludes with a chapter on visualization and an advanced overview of IDA processes.
Pang-Ning Tan, Michael Steinbach, Vipin Kumar
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.
Leo Breiman, Jerome Friedman, Charles J. Stone, R.A. Olshen
The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.