Solving Data Mining Problems Through Pattern Recognition

Solving Data Mining Problems Through Pattern Recognition

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Presents both theory and techniques behind the concepts and practice of pattern recognition. CD-ROM contains a fully functional trial version of Pattern Recognition Workbench -- a powerful, easy-to-use system with the latest machine learning, neural network, and statistical algorithms. It provides a complete working environment to solve your pattern recognition problems.
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Product details

  • Mixed media product | 400 pages
  • 172.72 x 233.68 x 35.56mm | 1,020.58g
  • Prentice Hall
  • Upper Saddle River, United States
  • English
  • 0130950831
  • 9780130950833

Table of contents

1. Introduction .

Pattern Recognition by Humans. Pattern Recognition by Computers. Data Mining and Pattern Recognition. Types of Pattern Recognition. Classification. Calculation in Classification. Uncertainty in Classification. Computer-Automated Classification.Estimation. Calculation in Estimation. Uncertainty in Estimation. Computer-Automated Estimation. Developing a Model. Fixed Models. Parametric Models. Nonparametric Models. Preprocessing. A Continuum of Methods. Biases Due to Prior Knowledge. The Purpose of this Book.

2. Key Concepts: Estimation.

Terminology and Notation. Characteristics of an Optimal Model. Sources of Error. Fixed Models. Parametric Models. Example: Linear Regression. Generalization. Shortcomings of Parametric Methods. Iteration through Parametric Forms. Nonparametric Models. The Underlying Modeling Problem. Heuristics in Nonparametric Modeling. Approximation Architectures. A Practical Nonparametric Approach. The Role of Preprocessing. Statistical Considerations.

3. Key Concepts: Classification.

Terminology and Notation. Characteristics of an Optimal Classifier. Types of Models. Decision-Region Boundaries. Probability Density Functions. Posterior Probabilities. Approaches to Modeling. Fixed Models. Parametric Models. Nonparametric Models. The Role of Preprocessing. The Importance of Multiple Techniques.

Appendix. Statistical Considerations.

4. Additional Application Areas.

Database Marketing. Response Modeling. Cross Selling. Time-Series Prediction. Detection. Probability Estimation. Information Compression. Sensitivity Analysis.

5. Overview of the Development Process.

Defining the Pattern Recognition Problem. Collecting Data. Preparing Data. Preprocessing. Selecting an Algorithm and Training Parameters. Training and Testing. Iterating Steps and Troubleshooting.

Appendix. Evaluating the Final Model.

6. Defining the Pattern Recognition Problem.

What Problems Are Suitable for Data-Driven Solutions? How Do You Evaluate Results? Is It a Classification or Estimation Problem?What Are the Inputs and Outputs?

Appendix. Defining the Problem in PRW.

7. Collecting Data.

What Data to Collect. How to Collect Data. How Much Data Is Enough. Using Simulated Data.

Appendix. Importing Data into PRW.

8. Preparing Data.

Transforming Data into Numerical Values. Inconsistent Data and Outliers.

Appendix. Preparing Data in PRW.

Handling Missing Data. Converting Non-Numeric Inputs. Handling Inconsistent Data or Outliers.

9. Data Preprocessing.

Why Should You Preprocess Your Data? Averaging Data Values. Thresholding Data. Reducing the Input Space. Normalizing Data. Why Normalize Data? Types of Normalization. Modifying Prior Probabilities. Other Considerations.

Appendix A: Preprocessing in PRW.

Averaging Time-Series Data. Thresholding and Replacing Input Values. Reducing the Input Space. Normalizing Data. Modifying Prior Input Probabilities.

10. Selecting Architectures and Training Parameters.

Types of Algorithms. How to Pick an Algorithm.Practical Constraints. Memory Usage. Training Times. Classification/Estimation Times. Algorithm Descriptions. Linear Regression. Logistic Regression. Unimodal Gaussian. Multilayered Perceptron/Backpropagation. Radial Basis Functions. K Nearest Neighbors. Gaussian Mixture. Nearest Cluster. K Means Clustering. Decision Trees. Other Nonparametric Architectures. Algorithm Comparison Summary.

Appendix A: Selecting Algorithms and Training Parameters in PRW.

Selecting an Algorithm in PRW. Setting Algorithm Parameters. Linear Regression. Logistic Regression. Unimodal Gaussian. Backpropagation/MLP. Radial Basis Functions. K Nearest Neighbors. Gaussian Mixture. Nearest Cluster. K Means Clustering.

11. Training and Testing.

Train, Test, and Evaluation Sets. Validation Techniques. Cross Validation. Bootstrap Validation. Sliding Window Validation.

Appendix A: Training, Testing, and Reporting in PRW.

The Experiment Manager. Running Experiments. Enabling and Disabling Experiments. Scheduling Experiments. Selecting Report Options. Viewing Different Reports. Cross Validation. Sliding Window Validation.

12. Iterating Steps and Trouble-Shooting.

Iterating to Improve Your Solution. Automated Searches.Input Variable Selection. Algorithm Parameter Searches. Trouble-Shooting. Training Error Is High. Test Error Is High. Classification Problem Performs Poorly on Some Classes. Problems with Production Accuracy. Decision Tree Works Best by Far. Backpropagation Does Not Converge. Backpropagation Finds a Local Minimum Solution. Matrix Inversion Problem. Unimodal Gaussian Has High Training Error. Gaussian Mixture Diverges. RBF Has High Training Error.

Appendix A: Iterating in PRW.

Overview of PRW Features. Creating Multiple Spreadsheets. Creating Multiple Experiment Managers. Using Multiple Work Sessions. Using Automated Searches. Preprocessing Data. Exporting Experiments and Reports. Re-Using Experiment Parameters. Building User Functions.

Appendix A: References and Suggested Reading.

Appendix B: Pattern Recognition Workbench.

Appendix C: Unica Technologies, Inc.

About Unica. Unica's Software Products.

Appendix D: Glossary.


Software License Agreement.

What's on this CD.
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