Max Bramer is Professor of Information Technology at the University of Portsmouth, UK. He has been actively involved in research in artificial intelligence since the early 1970s and leads the University of Portsmouth's Artificial Intelligence Research Group. His current research interests include knowledge discovery and data mining and model-based approaches to diagnostic reasoning. He has recently developed the Inducer system, which combines a number of paradigms for the automatic generation of classification rules from examples.He is Chairman of BCS-SGES and is a member of the lEE's Professional Group on Artificial Intelligence.

• Part I: Knowledge discovery and data mining in theoryChapter 1: Estimating concept difficulty with cross entropyChapter 2: Analysing outliers by searching for plausible hypothesesChapter 3: Attribute-value distribution as a technique for increasing the efficiency of data miningChapter 4: Using background knowledge with attribute-oriented data miningChapter 5: A development framework for temporal data miningChapter 6: An integrated architecture for OLAP and data miningPart II: Knowledge discovery and data mining in practiceChapter 7: Empirical studies of the knowledge discovery approach to health-information analysisChapter 8: Direct knowledge discovery and interpretation from a multilayer perceptron network which performs low-back-pain classificationChapter 9: Discovering knowledge from low-quality meteorological databasesChapter 10: A meteorological knowledge-discovery environmentChapter 11: Mining the organic compound jungle - a functional programming approachChapter 12: Data mining with neural networks - an applied example in understanding electricity consumption

Max Bramer is Professor of Information Technology at the University of Portsmouth, UK. He has been actively involved in research in artificial intelligence since the early 1970s and leads the University of Portsmouth's Artificial Intelligence Research Group. His current research interests include knowledge discovery and data mining and model-based approaches to diagnostic reasoning. He has recently developed the Inducer system, which combines a number of paradigms for the automatic generation of classification rules from examples.He is Chairman of BCS-SGES and is a member of the lEE's Professional Group on Artificial Intelligence.

• Part I: Knowledge discovery and data mining in theoryChapter 1: Estimating concept difficulty with cross entropyChapter 2: Analysing outliers by searching for plausible hypothesesChapter 3: Attribute-value distribution as a technique for increasing the efficiency of data miningChapter 4: Using background knowledge with attribute-oriented data miningChapter 5: A development framework for temporal data miningChapter 6: An integrated architecture for OLAP and data miningPart II: Knowledge discovery and data mining in practiceChapter 7: Empirical studies of the knowledge discovery approach to health-information analysisChapter 8: Direct knowledge discovery and interpretation from a multilayer perceptron network which performs low-back-pain classificationChapter 9: Discovering knowledge from low-quality meteorological databasesChapter 10: A meteorological knowledge-discovery environmentChapter 11: Mining the organic compound jungle - a functional programming approachChapter 12: Data mining with neural networks - an applied example in understanding electricity consumption

Max Bramer is Professor of Information Technology at the University of Portsmouth, UK. He has been actively involved in research in artificial intelligence since the early 1970s and leads the University of Portsmouth's Artificial Intelligence Research Group. His current research interests include knowledge discovery and data mining and model-based approaches to diagnostic reasoning. He has recently developed the Inducer system, which combines a number of paradigms for the automatic generation of classification rules from examples.He is Chairman of BCS-SGES and is a member of the lEE's Professional Group on Artificial Intelligence.

• Part I: Knowledge discovery and data mining in theoryChapter 1: Estimating concept difficulty with cross entropyChapter 2: Analysing outliers by searching for plausible hypothesesChapter 3: Attribute-value distribution as a technique for increasing the efficiency of data miningChapter 4: Using background knowledge with attribute-oriented data miningChapter 5: A development framework for temporal data miningChapter 6: An integrated architecture for OLAP and data miningPart II: Knowledge discovery and data mining in practiceChapter 7: Empirical studies of the knowledge discovery approach to health-information analysisChapter 8: Direct knowledge discovery and interpretation from a multilayer perceptron network which performs low-back-pain classificationChapter 9: Discovering knowledge from low-quality meteorological databasesChapter 10: A meteorological knowledge-discovery environmentChapter 11: Mining the organic compound jungle - a functional programming approachChapter 12: Data mining with neural networks - an applied example in understanding electricity consumption