| Description |
1 volume (various pagings) : illustrations ; 25 cm |
| Series |
Data Warehousing Institute series from Prentice Hall PTR |
|
Data Warehousing Institute series from Prentice Hall PTR.
|
| Contents |
Ch. 1. Introduction -- Ch. 2. Key Concepts: Estimation -- Ch. 3. Key Concepts: Classification -- Ch. 4. Additional Application Areas -- Ch. 5. Overview of the Development Process -- Ch. 6. Defining the Pattern Recognition Problem -- Ch. 7. Collecting Data -- Ch. 8. Preparing Data -- Ch. 9. Data Preprocessing -- Ch. 10. Selecting Architectures and Training Parameters -- Ch. 11. Training and Testing -- Ch. 12. Iterating Steps and Trouble-Shooting -- App. B. Pattern Recognition Workbench -- App. C. Unica Technologies, Inc |
| Summary |
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; and 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, and 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 |
| Notes |
Includes {2} p. of errata |
| Bibliography |
Includes bibliographical references and index |
| Notes |
System requirements for accompanying computer disc: MS-Windows 95 or Windows NT 3.51+ |
| Subject |
Pattern recognition systems.
|
|
Data mining.
|
| Author |
Kennedy, Ruby L.
|
| LC no. |
97042065 |
| ISBN |
0130950831 alkaline paper |
|
