The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is 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. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

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Data Mining: Concepts and Techniques

The book Knowledge Discovery in Databases, edited by Piatetsky-Shapiro and Frawley [PSF91], is an early collection of research papers on knowledge discovery from data. The book Advances in Knowledge Discovery and Data Mining, edited by Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy [FPSSe96], is a collection of later research results on knowledge discovery and data mining. There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99], Building Data Mining Applications for CRM by Berson, Smith, and Thearling [BST99], Data Mining: Practical Machine Learning Tools and Techniques by Witten and Frank [WF05], Principles of Data Mining (Adaptive Computation and Machine Learning) by Hand, Mannila, and Smyth [HMS01], The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman [HTF01], Data Mining: Introductory and Advanced Topics by Dunham, and Data Mining: Multimedia, Soft Computing, and Bioinformatics by Mitra and Acharya [MA03]. There are also books containing collections of papers on particular aspects of knowledge discovery, such as Machine Learning and Data Mining: Methods and Applications edited by Michalski, Brakto, and Kubat [MBK98], and Relational Data Mining edited by Dzeroski and Lavrac [De01], as well as many tutorial notes on data mining in major database, data mining and machine learning conferences.

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Data Mining: Concepts and Techniques (2nd edition)

A method and system for placing an order to purchase an item via the Internet. The order is placed by a purchaser at a client system and received by a server system. The server system receives purchaser information including identification of the purchaser, payment information, and shipment information from the client system. The server system then assigns a client identifier to the client system and associates the assigned client identifier with the received purchaser information. The server system sends to the client system the assigned client identifier and an HTML document identifying the item and including an order button. The client system receives and stores the assigned client identifier and receives and displays the HTML document. In response to the selection of the order button, the client system sends to the server system a request to purchase the identified item. The server system receives the request and combines the purchaser information associated with the client identifier of the client system to generate an order to purchase the item in accordance with the billing and shipment information whereby the purchaser effects the ordering of the product by selection of the order button.

Method and system for placing a purchase order via a communications network

As the size of software systems increases, the algorithms and data structures of the computation no longer constitute the major design problems. When systems are constructed from many components, the organization of the overall system -- the software architecture -- presents a new set of design problems. This level of design has been addressed in a number of ways including informal diagrams and descriptive terms, module interconnection languages, templates and frameworks for systems that serve the needs of specific domains, and formal models of component integration mechanisms. In this paper we provide an introduction to the emerging field of software architecture. We begin by considering a number of common architectural styles upon which many systems are currently based and show how different styles can be combined in a single design. Then we present six case studies to illustrate how architectural representations can improve our understanding of complex software systems. Finally, we survey some of the outstanding problems in the field, and consider a few of the promising research directions.

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An Introduction to Software Architecture

Data Mining: Concepts, Models, Methods, and Algorithms

From the Publisher:
How data mining delivers a powerful competitive advantage!
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)!
How data mining delivers a powerful competitive advantage! 
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 thatquickly 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)!

Building Data Mining Applications for CRM

From the Publisher:
Optimize your organization's data delivery system! Improving data delivery is a top priority in business computing today. This comprehensive,cutting-edge guide can helpby showing you how to effectively integrate data mining and other powerful data warehousing technologies. You'll learn how to: Use data warehousing to establish a competitive advantage; Solve business problems faster by exploiting online analytical processing (OLAP); Evaluate various data warehousing solutions (including SMP and MPP,parallel database management systems,metadata,OLAP,etc. ); Leverage your data warehousing utility via the Internet,client/server computing,and various data mining tools. In addition to providing a detailed overview and strategic analysis of the available data warehousing technologies,the book serves as a practical guide to data warehouse database design,star and snowflake schema approaches,multidimensional and mutirelational models,advanced indexing techniques,and data mining. You'll also learn how to compare different data mine technologies and products,and understand how they fit into your overall business and data processes. Intended for IS professionals as well as strategic planners,this fascinating book can be well relied upon as the essential reference to the standards,tools,technologiesand possibilitiesof data warehousing today

Data Warehousing, Data Mining, and OLAP

Introduction to client/server computing model approach to distribution client specialization in client/server environment server specialization communication systems local area networking TCP/IP and SNA Middleware distributed data management designing distributed database management systems data distribution and data replication DBMS architecture and implementation distributed transaction processing systems management data warehouse C/S application development C/S architecture for tomorrow.

Client/server architecture

Transform your business into a customer-centric enterprise 

Gain a complete and timely understanding of your customers using MDM-CDI and the real-world information contained in this comprehensive volume. Master Data Management and Customer Data Integration for a Global Enterprise explains how to grow revenue, reduce administrative costs, and improve client retention by adopting a customer-focused business framework.

Learn to build and use customer hubs and associated technologies, secure and protect confidential corporate and customer information, provide personalized services, and set up an effective data governance team. You'll also get full details on regulatory compliance and the latest pre-packaged MDM-CDI software solutions.

Design and implement a dynamic MDM-CDI architecture that fits the needs of your business
Implement MDM-CDI holistically as an integrated multi-disciplinary set of technologies, services, and processes 
Improve solution agility and flexibility using SOA and Web services
Recognize customers and their relationships with the enterprise across channels and lines of business
Ensure compliance with local, state, federal, and international regulations  
Deploy network, perimeter, platform, application, data, and user-level security 
Protect against identity and data theft, worm infection, and phishing and pharming scams
Create an Enterprise Information Governance Group
Perform development, QA, and business acceptance testing and data verification 


Table of contents

Foreword
Acknowledgments
Introduction
Part I. Introduction to Master Data Management and Customer Data Integration
Chapter 1. Overview of Master Data Management and Customer Data Integration
Chapter 2. CDI: Overview of Market Drivers and Key Challenges
Chapter 3. Challenges, Concerns, and Risks of Moving Toward Customer Centricity
Part II. Architectural Considerations
Chapter 4. CDI Architecture and Data Hub Components
Chapter 5. Architecting for Customer Data Integration
Chapter 6. Data Management Concerns of MDM/CDI Architecture
Part III. Data Security, Privacy, and Regulatory Compliance
Chapter 7. Overview of Risk Management for Integrated Customer Information
Chapter 8. Introduction to Information Security and Identity Management
Chapter 9. Protecting Content for Secure Master Data Management
Chapter 10. Enterprise Security and Data Visibility in Master Data Management Environments
Part IV. Implementing Customer Data Integration for the Enterprise
Chapter 11. Project Initiation
Chapter 12. Customer Identification
Chapter 13. Beyond Party Match: Merge, Split, Party Groups, and Relationships
Chapter 14. Data Governance, Standards, Information Quality, and Validation
Chapter 15. Data Synchronization
Chapter 16. Additional Implementation Considerations
Part V. Master Data Management: Market, Trends, and Directions
Chapter 17. MDM/CDI Vendors and Products Landscape 
Chapter 18. Where Do We Go From Here?Appendix A. List of AcronymsAppendix B. GlossaryAppendix C. Regulations and Compliance Rules Impacting Master Data Management and Customer Data Integration Projects
Index

Alex Berson

Papers

Building Data Mining Applications for CRM

Data Warehousing, Data Mining, and OLAP

Client/server architecture

Master Data Management and Customer Data Integration for a Global Enterprise

Master Data Management and Data Governance