Data mining is often referred to by real-time users and software solutions providers as knowledge discovery in databases (KDD). Good data mining practice for business intelligence (the art of turning raw software into meaningful information) is demonstrated by the many new techniques and developments in the conversion of fresh scientific discovery into widely accessible software solutions. This book has been written as an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining.Suitable for advanced undergraduates and their tutors at postgraduate level in a wide area of computer science and technology topics as well as researchers looking to adapt various algorithms for particular data mining tasks. A valuable addition to the libraries and bookshelves of the many companies who are using the principles of data mining (or KDD) to effectively deliver solid business and industry solutions.

Table of Contents:
Foreword Preface Acknowledgements Chapter 1: Introduction 1.1 THE NAME OF THE GAME 1.2 OVERVIEW OF MACHINE LEARNING METHODS 1.3 HISTORY OF MACHINE LEARNING 1.4 SOME EARLY SUCCESSES 1.5 APPLICATIONS OF MACHINE LEARNING 1.6 DATA MINING TOOLS AND STANDARDS 1.7 SUMMARY AND FURTHER READING Chapter 2: Learning and Intelligence 2.1 WHAT IS LEARNING 2.2 NATURAL LEARNING 2.3 LEARNING, INTELLIGENCE, CONSCIOUSNESS 2.4 WHY MACHINE LEARNING 2.5 SUMMARY AND FURTHER READING Chapter 3: Machine Learning Basics 3.1 BASIC PRINCIPLES 3.2 MEASURES FOR PERFORMANCE EVALUATION 3.3 ESTIMATING PERFORMANCE 3.4 *COMPARING PERFORMANCE OF MACHINE LEARNING ALGORITHMS 3.5 COMBINING SEVERAL MACHINE LEARNING ALGORITHMS 3.6 SUMMARY AND FURTHER READING Chapter 4: Knowledge Representation 4.1 PROPOSITIONAL CALCULUS 4.2 *FIRST ORDER PREDICATE CALCULUS 4.3 DISCRIMINANT AND REGRESSION FUNCTIONS 4.4 PROBABILITY DISTRIBUTIONS 4.5 SUMMARY AND FURTHER READING Chapter 5: Learning as Search 5.1 EXHAUSTIVE SEARCH 5.2 BOUNDED EXHAUSTIVE SEARCH (BRANCH AND BOUND) 5.3 BEST-FIRST SEARCH 5.4 GREEDY SEARCH 5.5 BEAM SEARCH 5.6 LOCAL OPTIMIZATION 5.7 GRADIENT SEARCH 5.8 SIMULATED ANNEALING 5.9 GENETIC ALGORITHMS 5.10 SUMMARY AND FURTHER READING Chapter 6: Measures for Evaluating the Quality of Attributes 6.1 MEASURES FOR CLASSIFICATION AND RELATIONAL PROBLEMS 6.2 MEASURES FOR REGRESSION 6.3 **FORMAL DERIVATIONS AND PROOFS 6.4 SUMMARY AND FURTHER READING Chapter 7: Data Preprocessing 7.1 REPRESENTATION OF COMPLEX STRUCTURES 7.2 DISCRETIZATION OF CONTINUOUS ATTRIBUTES 7.3 ATTRIBUTE BINARIZATION 7.4 TRANSFORMING DISCRETE ATTRIBUTES INTO CONTINUOUS 7.5 DEALING WITH MISSING VALUES 7.6 VISUALIZATION 7.7 DIMENSIONALITY REDUCTION 7.8 **FORMAL DERIVATIONS AND PROOFS 7.9 SUMMARY AND FURTHER READING Chapter 8: *Constructive Induction 8.1 DEPENDENCE OF ATTRIBUTES 8.2 CONSTRUCTIVE INDUCTION WITH PRE-DEFINED OPERATORS 8.3 CONSTRUCTIVE INDUCTION WITHOUT PRE-DEFINED OPERATORS 8.4 SUMMARY AND FURTHER READING Chapter 9: Symbolic Learning 9.1 LEARNING OF DECISION TREES 9.2 LEARNING OF DECISION RULES 9.3 LEARNING OF ASSOCIATION RULES 9.4 LEARNING OF REGRESSION TREES 9.5 *INDUCTIVE LOGIC PROGRAMMING 9.6 NAIVE AND SEMI-NAIVE BAYESIAN CLASSIFIER 9.7 BAYESIAN BELIEF NETWORKS 9.8 SUMMARY AND FURTHER READING Chapter 10: Statistical Learning 10.1 NEAREST NEIGHBORS 10.2 DISCRIMINANT ANALYSIS 10.3 LINEAR REGRESSION 10.4 LOGISTIC REGRESSION 10.5 *SUPPORT VECTOR MACHINES 10.6 SUMMARY AND FURTHER READING Chapter 11: Artificial Neural Networks 11.1 INTRODUCTION 11.2 TYPES OF ARTIFICIAL NEURAL NETWORKS 11.3 *HOPFIELD’S NEURAL NETWORK 11.4 *BAYESIAN NEURAL NETWORK 11.5 PERCEPTRON 11.6 RADIAL BASIS FUNCTION NETWORKS 11.7 **FORMAL DERIVATIONS AND PROOFS 11.8 SUMMARY AND FURTHER READING Chapter 12: Cluster Analysis 12.1 INTRODUCTION 12.2 MEASURES OF DISSIMILARITY 12.3 HIERARCHICAL CLUSTERING 12.4 PARTITIONAL CLUSTERING 12.5 MODEL-BASED CLUSTERING 12.6 OTHER CLUSTERING METHODS 12.7 SUMMARY AND FURTHER READING Chapter 13: **Learning Theory 13.1 COMPUTABILITY THEORY AND RECURSIVE FUNCTIONS 13.2 FORMAL LEARNING THEORY 13.3 PROPERTIES OF LEARNING FUNCTIONS 13.4 PROPERTIES OF INPUT DATA 13.5 CONVERGENCE CRITERIA 13.6 IMPLICATIONS FOR MACHINE LEARNING 13.7 SUMMARY AND FURTHER READING Chapter 14: **Computational Learning Theory 14.1 INTRODUCTION 14.2 GENERAL FRAMEWORK FOR CONCEPT LEARNING 14.3 PAC LEARNING MODEL 14.4 VAPNIK-CHERVONENKIS DIMENSION 14.5 LEARNING IN THE PRESENCE OF NOISE 14.6 EXACT AND MISTAKE BOUNDED LEARNING MODELS 14.7 INHERENT UNPREDICTABILITY AND PAC-REDUCTIONS 14.8 WEAK AND STRONG LEARNING 14.9 SUMMARY AND FURTHER READING Appendix A: *Definitions of some lesser known terms A.1 COMPUTATIONAL COMPLEXITY CLASSES A.2 ASYMPTOTIC NOTATION A.3 SOME BOUNDS FOR PROBABILISTIC ANALYSIS A.4 COVARIANCE MATRIX References Index