Solving Data Mining Problems Through Pattern Recognition (Bk/CD)

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9780130950833: Solving Data Mining Problems Through Pattern Recognition (Bk/CD)

Data mining is an exploding technology increasingly used in major industries like finance, aerospace, and the medical industry. To truly take advantage of data mining capabilities, one must use and understand pattern recognition techniques. They are addressed in this book along with a tutorial on how to use the accompanying pattern software ("Pattern Recognition Workbench") on the CD-ROM. .

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From the Inside Flap:

Preface

Data Mining

Data mining is a term usually applied to techniques that can be used to find underlying structure and relationships in large amounts of data. These techniques are drawn primarily from the related fields of neural networks, statistics, pattern classification, and machine learning. They are becoming more important as computer automation spreads and as the processing and storage capabilities of computers increase. Widely available, low-cost computer technology now makes it possible to both collect historical data and also to institute on-line analysis and controls for newly arriving data.

Applications

Data mining techniques are being successfully used for many diverse applications. These include paper and sheet metal production control, medical diagnosis and risk prediction, credit-card fraud detection, computer security break-in and misuse detection, computer user identity verification, aluminum and steel smelting control, oil refinery control, pollution control in power plants, fraudulent income tax return detection, automobile engine control and fault detection, electric motor fault and failure prediction, mass mailing and telemarketing, and simplifying world-wide-web usage by predicting useful sites from past user behavior. Benefits of Data Mining

Benefits in these and other applications include reduced costs due to more accurate control, more accurate future predictions, more effective fault detection and prediction, fraud detection and control, and automation of repetitive human tasks. In addition, services can be improved and extended due to a better understanding of underlying processes and human behavior. Outline of this Book

This book provides a concise introduction to some of the most important input-output mapping, prediction, pattern classification, and clustering algorithms useful for data mining. This introduction is based on many collective years of experience by the authors, which has led to a focus on practical issues that must be addressed to successfully solve data mining problems. The book provides a basic road map for experts who know much about a specific application, but little about neural networks, statistics, pattern classification, or machine learning.

This road map first helps potential users determine whether input-output mapping, prediction, pattern classification, or clustering algorithms are appropriate for a given application. It then helps users determine which measurements, attributes, or features might be useful as inputs to these algorithms and provides guidance in collecting and formatting this data for computer analysis. Guidelines are then presented for accurately accessing performance using separate training, evaluation, and test data partitions or cross-validation. Finally, each important algorithm is described and guidance is provided concerning settings for parameters used to control the many algorithms. Multi-Algorithm Approach

An important truism presented in this book is that data mining is an art and that there is no single simple approach that is best for all problems. Rather, there are many algorithms and data representations, and the best strategy is to interactively experiment to find an approach that works for a particular data set. This human interaction is greatly simplified by the availability of software toolkits which allow users to interactively explore many algorithms on a common data set using the same performance metrics. This book focuses on one comprehensive software toolkit (Pattern Recognition Workbench) that includes most of the algorithms described and has the capability of handling large data sets. Details concerning this software, however, are relegated to the Appendix and to sections at the ends of chapters. These details can thus be skipped or used as examples of the types of information required to apply the various algorithms. Intended Audience

This book is most useful for persons who have a specific application in mind, but who know little about data mining algorithms. They can use this book to determine whether the algorithms presented can be applied to their application, to learn terminology, and to provide guidance when they try out some of the recommended approaches using a software toolkit. More experienced users who want to understand the theory behind prediction, mapping, control, pattern classification, and clustering or who would like to read detailed descriptions concerning specific data mining applications should explore other more advanced texts.

Richard P. Lippmann

From the Back Cover:

Apply pattern recognition to find the hidden gems in your data!

Data mining technology is helping businesses everywhere to work smarter by revealing unknown patterns within existing archives. Applying the latest advances in pattern recognition software can give you a key competitive edge across all data mining applications. The tutorials and software package included in Solving Data Mining Problems through Pattern Recognition take advantage of machine learning techniques and neural networks to help you get the most out of your data. Besides explaining the most current theories, Solving Data Mining Problems through Pattern Recognition takes a practical approach to overall project development concerns.

The rigorous, multi-step method includes:

  • Defining the pattern recognition problem
  • Collection, preparation, and preprocessing of data
  • Choosing the appropriate algorithm and tuning algorithm parameters Training, testing, and troubleshooting.

Pattern classification, estimation, and modeling are addressed using the following algorithms:

  • Linear and logistic regression
  • Unimodal Gaussian and Gaussian mixture
  • Multilayered perceptron/backpropagation and radial basis function neural networks
  • K nearest neighbors and nearest cluster
  • K means clustering.

While some aspects of pattern recognition involve advanced mathematical principles, most successful projects rely on a strong element of human experience and intuition. Solving Data Mining Problems through Pattern Recognition provides a strong theoretical grounding for beginners, yet it also contains detailed models and insights into real-world problem-solving that will inspire more experienced users, be they database designers, modelers, or project leaders.

This book includes a free, 90-day trial copy of Pattern Recognition Workbench, a powerful, easy-to-use system that combines machine learning, neural networks, and statistical algorithms to help you apply pattern recognition to your data right now. The enclosed CD-ROM runs under Windows(r) 95 and Windows NT(tm).

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