Kononenko I., Kukar M. Machine Learning and Data Mining: Introduction to Principles and Algorithms

Horwood Publishing Limited, 2007. — 475 p. — ISBN: 1904275214, 978-1904275213.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.
Provides an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining.
A valuable addition to the libraries and bookshelves of companies using the principles of data mining (or KDD) to effectively deliver solid business and industry solutions.The name of the game; Overview of machine learning methods; History of machine learning;
Some early successes; Applications of machine learning; Data mining tools and standards
Learning and Intelligence
What is learning; Natural learning, Learning, intelligence, consciousness; Why machine learning
Machine Learning Basics
Basic principles; Measures for performance evaluation; Estimating performance;
Comparing performance of ML algorithms; Combining several ML algorithms
Knowledge Representation
Propositional calculus; First order predicate calculus; Discriminant and regression functions; Probability distributions
Learning as Search
Exhaustive search; Bounded exhaustive search (branch and bound); Best-first search; Greedy search;
Beam search; Local optimization; Gradient search; Simulated annealing; Genetic algorithms
Attribute Quality Measures
Measures for classification; Measures for regression; Formal derivations and proofs
Data Preprocessing
Representation of complex structures; Discretization of continuous attributes; Attribute binarization;
Transforming discrete attributes into continuous; Dealing with missing values; Visualization;
Dimensionality reduction; Formal derivations and proofs
Constructive Induction
Dependence of attributes; Constructive induction with pre-defined operators; Constructive induction without pre-defined operators
Symbolic Learning
Learning of decision trees; Learning of decision rules; Learning of association rules; Learning of regression trees;
Inductive logic programming; Naive and semi-naive Bayesian classifier; Bayesian belief networks
Statistical Learning
Nearest neighbors; Discriminant analysis; Linear regression; Logistic regression; Support vector machines
Artificial Neural Networks
Introduction; Types of artificial neural networks; Hopfield's neural network; Bayesian neural network
Perceptron; Radial basis function networks; Formal derivations and proofs
Cluster Analysis
Introduction; Measures of dissimilarity; Hierarchical clustering; Partitional clustering;
Model-based clustering; Other clustering methods
Learning Theory
Computability theory and recursive functions; Formal learning theory; Properties of learning functions;
Properties of input data; Convergence criteria; Implications for machine learning
Computational Learning Theory
Introduction; General framework for concept learning; PAC Learning Model; Vapnik-Chervonenkis dimension;
Learning in the presence of noise; Exact and mistake bounded learning models; Inherent unpredictability and PAC-reductions;
Weak and strong learning
A: Definitions of some lesser known terms
Computational complexity classes; Asymptotic notation; Some bounds for probabilistic analysis; Covariance matrix