Written by a team of Oracle insiders, this authoritative book provides you with the most current coverage of the Oracle data warehousing platform as well as the full suite of business intelligence tools. You'll learn how to leverage Oracle features and how those features can be used to provide solutions to a variety of needs and demands. Plus, you'll get valuable tips and insight based on the authors' real-world experiences and their own implementations.

CD-ROM contains the actual models that are described in the book, providing examples of the most commonly asked data mining questions regarding marketing, sales, and customer support applications. Complete working code written in SAS, the most popular mining modeling language, is also provided. Readers can use the CD-ROM to directly implement the models for business use--resulting in better campaigns, improved customer service, and increased profits. A step-by-step guide showing how to create an d implement models of the most commonly asked mining questions from a variety of disciplines. Provides proven techniques and numerous case studies that detail available data sources for developing target models. A walk through entire process of a data mining project.

Here's the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. Data Mining: Concepts and Techniques equips you with a sound understanding of data mining principles and teaches you proven methods for knowledge discovery in large corporate databases. Written expressly for database practitioners and professionals, this book begins with a conceptual introduction designed to get you up to speed. This is followed by a comprehensive and state-of-the-art coverage of data mining concepts and techniques. Each chapter functions as a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. Wherever possible, the authors raise and answer questions of utility, feasibility, optimization, and scalability, keeping your eye on the issues that will affect your project's results and your overall success. Data Mining: Concepts and Techniques is the master reference that practitioners and researchers have long been seeking. It is also the obvious choice for academic and professional classrooms.

In their critically acclaimed book, Data Mining Techniques, Michael Berry and Gordon Linoff showed readers how to use data mining techniques to improve marketing and sales. In their new, book they take readers to the next level with a series of step-by-step lessons, built around 20 real-world cases illustrating how they used their techniques to solve problems.

Are you fully harnessing the power of information to support management and marketing decisions? You will, with this one-stop guide to choosing the right tools and technologies for a state-of-the-art data management strategy built on a Customer Relationship Management (CRM) framework. Authors Alex Berson, Stephen Smith, and Kurt Thearling help you understand the principles of data warehousing and data mining systems, and carefully spell out techniques for applying them so that your business gets the biggest pay-off possible. Find out about Online Analytical Processing (OLAP) tools that quickly navigate within your collected data. Explore privacy and legal issues...evaluate current data mining application packages...and let real-world examples show you how data mining can impact -- and improve -- all of your key business processes. Start uncovering your best prospects and offering them the products they really want (not what you think they want)!

This text, Data Mining: Building Competitive Advantage, resulted from the revelation that data mining is becoming mainstream and that there are few books about data mining devoted to the business professional. It provides an innovative, easy approach to learning data mining for business professionals, students, and consultants. The CD-ROM at the back of the book makes learning data mining a hands-on activity. You can try out different software packages available for data mining and learn how the se tools are being used to solve industry problems. This book focuses on how knowledge discovery is used in different industries, and discusses several of the data-mining software products available. Sample studies are provided for specific industries, including retail, banking, insurance, and healthcare. This text takes a different approach to introducing data mining than the academic books currently on the market. The focus of this book is on industry applications, discussions of specific busi ness problems, and a hands-on teaching style to demonstrate how tools can be used to attain business benefit.

Data mining techniques are used to power intelligent software, both on and off the Internet. Data Mining: Practical Machine Learning Tools explains the magic behind information extraction in a book that succeeds at bringing the latest in computer science research to any IS manager or developer. In addition, this book provides an opportunity for the authors to showcase their powerful reusable Java class library for building custom data mining software.This text is remarkable with its comprehensiv e review of recent research on machine learning, all told in a very approachable style. (While there is plenty of math in some sections, the authors' explanations are always clear.) The book tours the nature of machine learning and how it can be used to find predictive patterns in data comprehensible to managers and developers alike. And they use sample data (for such topics as weather, contact lens prescriptions, and flowers) to illustrate key concepts. After setting out to explain the types of machine learning models (like decision trees and classification rules), the book surveys algorithms used to implement them, plus strategies for improving performance and the reliability of results. Later the book turns to the authors' downloadable Weka (rhymes with "Mecca") Java class library, which lets you experiment with data mining hands-on and gets you started with this technology in custom applications. Final sections look at the bright prospects for data mining and machine learning on th e Internet (for example, in Web search engines).

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