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Data Mining, Southeast Asia Edition

Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data— including stream data, sequence data, graph structured data, social network data, and multi-relational data. A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects Complete classroom support for instructors at www.mkp.com/datamining2e companion site

Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, ...

Data Mining and Knowledge Discovery Handbook

Data Mining and Knowledge Discovery Handbookorganizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. Data Mining and Knowledge Discovery Handbookis designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.

Different models are compared, and possibly combined. In the evaluation phase
models are selected and reviewed according to the business criteria. The whole
Data Mining process is reviewed and a list of possible actions is elaborated.

Data Mining Your Website

Web developers and sales people can get a jump on the next generation of Web marketing with this guide to data mining that shows how to examine large pools of customer data and identify patterns in order to design Web sites that better target potential customers.

The mining of your web data starts well before you begin using a data mining tool
, and it continues well after your analysis. Several steps are involved prior to the
actual analysis of the data in order to ensure the data mining process is a ...

Handbook of Educational Data Mining

Handbook of Educational Data Mining (EDM) provides a thorough overview of the current state of knowledge in this area. The first part of the book includes nine surveys and tutorials on the principal data mining techniques that have been applied in education. The second part presents a set of 25 case studies that give a rich overview of the problems that EDM has addressed. Researchers at the Forefront of the Field Discuss Essential Topics and the Latest Advances With contributions by well-known researchers from a variety of fields, the book reflects the multidisciplinary nature of the EDM community. It brings the educational and data mining communities together, helping education experts understand what types of questions EDM can address and helping data miners understand what types of questions are important to educational design and educational decision making. Encouraging readers to integrate EDM into their research and practice, this timely handbook offers a broad, accessible treatment of essential EDM techniques and applications. It provides an excellent first step for newcomers to the EDM community and for active researchers to keep abreast of recent developments in the field.

The first part of the book includes nine surveys and tutorials on the principal data mining techniques that have been applied in education.

Introduction to Data Mining and Its Applications

This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in data base systems and new data base applications and is also designed to give a broad, yet in-depth overview of the field of data mining. Data mining is a multidisciplinary field, drawing work from areas including database technology, AI, machine learning, NN, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing and data visualization. This book is intended for a wide audience of readers who are not necessarily experts in data warehousing and data mining, but are interested in receiving a general introduction to these areas and their many practical applications. Since data mining technology has become a hot topic not only among academic students but also for decision makers, it provides valuable hidden business and scientific intelligence from a large amount of historical data. It is also written for technical managers and executives as well as for technologists interested in learning about data mining.

A.9.3 Groupware, Security, and Persistent Objects In an enterprise where
information is often shared within a workgroup, it is important that a data mining
system supports the exchange of information in order to enhance productivity.
Therefore ...

Advances in web intelligence and data mining

The Internet has become a major communication medium, where virtually any kind of content can be transferred instantly and reliably between individual users and entire organizations located in any part of the globe. Consequently, more effective and efficient methods and technologies are needed to make the most of the Web's nearly unlimited potential. The new Web-related research directions include intelligent methods usually associated with the fields of computational intelligence, soft computing, and data mining. This book presents state-of-the-art developments in the area of computationally intelligent methods applied to various aspects and ways of Web exploration and Web mining. Some novel data mining algorithms that can lead to more effective and intelligent Web-based systems are also described. Scientists, engineers, and research students are expected to find many inspiring ideas in this volume.

This book presents state-of-the-art developments in the area of computationally intelligent methods applied to various aspects and ways of Web exploration and Web mining.

Data Mining

Opportunities and Challenges

Data Mining: Opportunities and Challenges presents an overview of the state of the art approaches in this new and multidisciplinary field of data mining. The primary objective of this book is to explore the myriad issues regarding data mining, specifically focusing on those areas that explore new methodologies or examine case studies. This book contains numerous chapters written by an international team of forty-four experts representing leading scientists and talented young scholars from seven different countries.

This chapter presents an approach to mining free-text documents for structure
that is qualitative in nature. It complements the statistical and machine-learning
approaches, insomuch as the structural organization of information in documents
is ...

Microsoft Data Mining

Integrated Business Intelligence for E-Commerce and Knowledge Management

This guide teaches data mining from the perspective of IT professionals using Microsoft data management and e-commerce technologies. The book explains major new data mining capabilities in the forthcoming SQL Server 2000, Microsoft Commerce Server, and other products, and details the new Microsoft standard, "OLE DB for Data Mining".

Tom DeMarco Pulling data together into an analysis environment — called here
a mining mart — is an essential precondition to providing data in the right form
and providing the right measurements in order to produce a timely and useful ...

Data Mining and Business Intelligence

A Guide to Productivity

Annotation Provides an overview of data mining technology and how it is applied in a business environment. Material is not written in a technical style, but rather addresses the applied methodology behind implementing data mining techniques in the corporate environment. Explains how the technology evolved, overviews the methodologies that comprise the data mining spectrum, and looks at everyday business applications for data mining, in areas such as marketing and advertising promotions and pricing policies using econometric-based modeling, and using the Internet to help improve an organization's performance. Kudyba is an economic consultant. Hoptroff is an independent consultant with experience in data mining software. Annotation c. Book News, Inc., Portland, OR (booknews.com).

At this juncture it is appropriate to offer a more formal definition of data mining
before we progress to a more thorough description of the techniques,
applications and general methodologies of the data mining process in the world
of Business ...

Data Mining Techniques

This Book Addresses All The Major And Latest Techniques Of Data Mining And Data Warehousing. It Deals With The Latest Algorithms For Discussing Association Rules, Decision Trees, Clustering, Neural Networks And Genetic Algorithms. The Book Also Discusses The Mining Of Web Data, Temporal And Text Data. It Can Serve As A Textbook For Students Of Compuer Science, Mathematical Science And Management Science, And Also Be An Excellent Handbook For Researchers In The Area Of Data Mining And Warehousing.

This Book Addresses All The Major And Latest Techniques Of Data Mining And Data Warehousing.