| Description |
1 online resource (xxxiii, 350 pages) |
| Series |
Springer Optimization and Its Applications ; v. 43 |
|
Springer optimization and its applications ; v. 43.
|
| Contents |
Foreword; Preface; Acknowledgments; List of Figures; List of Tables; Part I Algorithmic Issues; Introduction; Inferring a Boolean Function from Positive and Negative Examples; A Revised Branch-and-Bound Approach for Inferring a Boolean Function from Examples; Some Fast Heuristics for Inferring a Boolean Function from Examples; An Approach to Guided Learning of Boolean Functions; An Incremental Learning Algorithm for Inferring Boolean Functions; A Duality Relationship Between Boolean Functions in CNF and DNF Derivable from the Same Training Examples |
| Summary |
There are many approaches to data mining and knowledge discovery (DM & KD), including neural networks, closest neighbor methods, and various statistical methods. This monograph, however, focuses on the development and use of a novel approach, based on mathematical logic, that the author and his research associates have worked on over the last 20 years. The methods presented in the book deal with key DM & KD issues in an intuitive manner and in a natural sequence. Compared to other DM & KD methods, those based on mathematical logic offer a direct and often intuitive approach for extracting easily int |
| Bibliography |
Includes bibliographical references (pages 317-333) and indexes |
| Notes |
Print version record |
| In |
Springer eBooks |
| Subject |
Data mining.
|
|
computer science.
|
|
data processing.
|
|
COMPUTERS -- Programming Languages -- SQL.
|
|
Informática
|
|
Datos-Tratamiento
|
|
Data mining
|
| Form |
Electronic book
|
| ISBN |
9781441916303 |
|
144191630X |
|
