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Data Mining for Business Applications

Data Mining for Business Applications presents the state-of-the-art research and development outcomes on methodologies, techniques, approaches and successful applications in the area. The contributions mark a paradigm shift from “data-centered pattern mining” to “domain driven actionable knowledge discovery” for next-generation KDD research and applications. The contents identify how KDD techniques can better contribute to critical domain problems in theory and practice, and strengthen business intelligence in complex enterprise applications. The volume also explores challenges and directions for future research and development in the dialogue between academia and business.

http://www.paid4share.net/file/9863/9780387794198-0387794190-rar.html

[Admin]

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition

As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work.

The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more.
* Algorithmic methods at the heart of successful data miningincluding tried and true techniques as well as leading edge methods
* Performance improvement techniques that work by transforming the input or output
* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualizationin a new, interactive interface

http://www.paid4share.net/file/2532/0120884070-rar.html

[Admin]

Applied Data Mining : Statistical Methods for Business and Industry

[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.

Data sets used in the case studies are available at ftp://ftp.wiley.co.uk/pub/books/giudici

http://rapidshare.de/files/20903149/047084678X.zip

[Admin]

Discovering Knowledge in Data: An Introduction to Data Mining

Learn Data Mining by doing data mining
Data mining can be revolutionary-but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets.
Employing a "white box" methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include:
* Data preprocessing and classification
* Exploratory analysis
* Decision trees
* Neural and Kohonen networks
* Hierarchical and k-means clustering
* Association rules
* Model evaluation techniques
Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge.
An Instructor's Manual presenting detailed solutions to all the problems in the book is available online.

Learn Data Mining by doing data mining
Data mining can be revolutionary-but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets.
Employing a "white box" methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include:
* Data preprocessing and classification
* Exploratory analysis
* Decision trees
* Neural and Kohonen networks
* Hierarchical and k-means clustering
* Association rules
* Model evaluation techniques
Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge.

http://www.icefile.info/index.php?page=main&id=4cfd5045&name=0471666572.rar

[Admin]

Data Mining Using SAS Enterprise Miner

Data Mining Using SAS® Enterprise Miner introduces the reader to a wide variety of data mining techniques in SAS® Enterprise Miner. This first-of-a-kind book explains the purpose of -- and reasoning behind -- every node that is a part of Enterprise Miner with regard to SEMMA design and data mining analysis. Each chapter starts with a short introduction to the assortment of statistics that are generated from the various Enterprise Miner nodes, followed by detailed explanations of configuration settings that are located within each node. The end result of the author’s meticulous presentation is a well crafted study guide on the various methods that one employs to both randomly sample and partition data within the process flow of SAS® Enterprise Miner.

http://www.paid4share.net/file/7854/9780470149010-0470149019-rar.html 58MB

[Admin]

Data Mining Using SAS Applications

Most books on data mining focus on principles and furnish few instructions on how to carry out a data mining project. Data Mining Using SAS Applications not only introduces the key concepts but also enables readers to understand and successfully apply data mining methods using powerful yet user-friendly SAS macro-call files. These methods stress the use of visualization to thoroughly study the structure of data and check the validity of statistical models fitted to data. · Learn how to convert PC databases to SAS data · Discover sampling techniques to create training and validation samples · Understand frequency data analysis for categorical data · Explore supervised and unsupervised learning · Master exploratory graphical techniques · Acquire model validation techniques in regression and classification The text furnishes 13 easy-to-use SAS data mining macros designed to work with the standard SAS modules. No additional modules or previous experience in SAS programming is required. The author shows how to perform complete predictive modeling, including data exploration, model fitting, assumption checks, validation, and scoring new data, on SAS datasets in less than ten minutes!

http://www.icefile.net/index.php?page=main&id=0c62f2240&name=1584883456.rar (9 Mb)

[Mbwar]

Bonjour

c'est ma première intervention sur ce forum que je trouve formidable.

j'ai attéri ici à la suite de recherches interminable de ressources francophones sur le data mining notamment des livres.

le constat est sans équivoque. en dehors de la langue de shackespere, le data mining en francais "n'existe pas" d'après mes recherches.

j'aimerai profiter de votre expertise pour nous trouver quelques uns si vous en avez ou si vous conaissez des sites oû on peut trouver.

merci.

[stat_stick]

Bonjour,
Effectivement, il existe peu d'ouvrage sur le data mining en français, d'ailleurs, c'est toute la science statistique qui fonctionne en anglais, d'où son indispensable maîtrise.
Le livre de Stéphane Tufféry : "Data mining et statistique décisionnelle" constitue une bonne référence.

[serir]

Bonjour ,

Je vous prie si quelqu'un a le livre "data mining et statistique decisionnelle de Stephane Tuffery" format electronique, ou m'orienter vers un liens valable.

Bien merci.

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