附註:Includes bibliographical references (pages 199-210) and index.
COVER -- TABLE OF CONTENTS -- LIST OF FIGURES -- LIST OF TABLES -- PREFACE -- CHAPTER 1: INTRODUCTION -- 1.1. DATAMINING -- 1.2. MOTIVATION -- 1.3. CONTRIBUTIONS OF THE BOOK -- 1.4. OUTLINE OF THE BOOK -- CHAPTER 2: AN OVERVIEW OF DATA MINING -- 2.1 DECISION TREE APPROACH -- 2.2. CLASSIFICATION RULE -- 2.3. ASSOCIATION RULE -- 2.4 STATISTICAL APPROACH -- 2.5 BAYESIAN NETWORK LEARNING -- 2.6. OTHER APPROACHES -- CHAPTER 3: AN OVERVIEW ON EVOLUTIONARY ALGORITHMS -- 3.1. EVOLUTIONARY ALGORITHMS -- 3.2. GENETIC ALGORITHMS (GAs) -- 3.3. GENETIC PROGRAMMING (GP) -- 3.4. EVOLUTION STRATEGIES (ES) -- 3.5. EVOLUTIONARY PROGRAMMING (EP) -- CHAPTER 4: INDUCTIVE LOGIC PROGRAMMING -- 4.1. INDUCTIVE CONCEPT LEARNING -- 4.2. INDUCTIVE LOGIC PROGRAMMING (ILP) -- 4.3. TECHNIQUES AND METHODS OF ILP -- CHAPTER 5: THE LOGIC GRAMMARS BASED GENETIC PROGRAMMING SYSTEM (LOGENPRO) -- 5.1. LOGIC GRAMMARS -- 5.2. REPRESENTATIONS OF PROGRAMS -- 5.3. CROSSOVER OF PROGRAMS -- 5.4. MUTATION OF PROGRAMS -- 5.5. THE EVOLUTION PROCESS OF LOGENPRO -- 5.6. DISCUSSION -- CHAPTER 6: DATA MINING APPLICATIONS USING LOGENPRO -- 6.1. LEARNING FUNCTIONAL PROGRAMS -- 6.2. INDUCING DECISION TREES USING LOGENPRO -- 6.3. LEARNING LOGIC PROGRAM FROM IMPERFECT DATA -- CHAPTER 7: APPLYING LOGENPRO FOR RULE LEARNING -- 7.1. GRAMMAR -- 7.2. GENETIC OPERATORS -- 7.3. EVALUATION OF RULES -- 7.4. LEARNINGMULTIPLE RULES FROM DATA -- CHAPTER 8: MEDICAL DATA MINING -- 8.1. A CASE STUDY ON THE FRACTURE DATABASE -- 8.2. A CASE STUDY ON THE SCOLIOSIS DATABASE -- CHAPTER 9: CONCLUSION AND FUTURE WORK -- 9.1. CONCLUSION -- 9.2. FUTUREWORK -- APPENDIX A: THE RULE SETS DISCOVERED -- A.1. THE BEST RULE SET LEARNED FROM THE IRIS DATABASE -- A.2. THE BEST RULE SET LEARNED FROM THE MONK DATABASE -- A.3. THE BEST RULE SET LEARNED FROM THE FRACTURE DATABASE -- A.4. THE BEST RULE SET LEARNED FROM THE SCOLIOSIS DATABASE -- APPENDIX B: THE GRAMMAR USED FOR THE FRACTURE AND SCOLIOSIS DATABASES -- B.1. THE GRAMMAR FOR THE FRACTURE DATABA
摘要:Data mining involves the non-trivial extraction of implicit, previously unknown, and potentially useful information from databases. Genetic Programming (GP) and Inductive Logic Programming (ILP) are two of the approaches for data mining. This book first sets the necessary backgrounds for the reader, including an overview of data mining, evolutionary algorithms and inductive logic programming. It then describes a framework, called GGP (Generic Genetic Programming), that integrates GP and ILP based on a formalism of logic grammars. The formalism is powerful enough to represent context- sensitive information and domain-dependent knowledge. This knowledge can be used to accelerate the learning speed and/or improve the quality of the knowledge induced. A grammar-based genetic programming system called LOGENPRO (The LOGic grammar based GENetic PROgramming system) is detailed and tested on many problems in data mining. It is found that LOGENPRO outperforms some ILP systems. We have also illustrated how to apply LOGENPRO to emulate Automatically Defined Functions (ADFs) to discover problem representation primitives automatically. By employing various knowledge about the problem being solved, LOGENPRO can find a solution much faster than ADFs and the computation required by LOGENPRO is much smaller than that of ADFs. Moreover, LOGENPRO can emulate the effects of Strongly Type Genetic Programming and ADFs simultaneously and effortlessly. Data Mining Using Grammar Based Genetic Programming and Applications is appropriate for researchers, practitioners and clinicians interested in genetic programming, data mining, and the extraction of data from databases.
