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.
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"Readers are treated to a comprehensive look at the principles. …a fine overview of machine learning methods… Recommended." - Choice Magazine
About the Author
Igor Kononenko studied computer science at the University of Ljubliana, Slovenia, receiving his BSc in 1982, MSc in 1985 and PhD in 1990. He is now professor at the Faculty of Computer and Information Science there, teaching courses in Programming Languages, Algorithms and Data Structures; Introduction to Algorithms and Data Structures; Knowledge Engineering, Machine Learning and Knowledge Discovery in Databases. He is the head of the Laboratory for Cognitive Modelling and a member of the Artificial Intelligence Department at the same faculty. His research interests include artificial intelligence, machine learning, neural networks and cognitive modeling. He is the (co) author of 170 scientific papers in these fields and 10 textbooks. Professor Kononenko is a member of the editorial board of Applied Intelligence and Informatica journals and was also twice chair of the programme committee of the International Cognitive Conference in Ljubliana.
Matjaz Kukar studied computer science at the University of Ljubliana, Slovenia, receiving his BSc in 1993, MSc in 1996 and PhD in 2001. He is now the assistant professor at the Faculty of Computer and Information Science there and is also a member of the Artificial Intelligence Department at the same faculty. His research interests include knowledge discovery in databases, machine learning, artificial intelligence and statistics. Professor Kukar is the (co) author of over 50 scientific papers in these fields.
Matjaz Kukar studied computer science at the University of Ljubliana, Slovenia, receiving his BSc in 1993, MSc in 1996 and PhD in 2001. He is now the assistant professor at the Faculty of Computer and Information Science there and is also a member of the Artificial Intelligence Department at the same faculty. His research interests include knowledge discovery in databases, machine learning, artificial intelligence and statistics. Professor Kukar is the (co) author of over 50 scientific papers in these fields.
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Inside This Book
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Key Phrases - Statistically Improbable Phrases (SIPs):
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symbolic learning, data preprocessing, bounded exhaustive search, regressional problems, banner plot, recursive learners, attribute subgraph, learnability paradigm, positive learning examples, negative learning examples, swiss dataset, new binary attributes, query learning model, finite oracle, generalized delta learning rule, statistical query model, original attribute space, independent testing set, random classification noise, weighted majority algorithm, abandoned hypotheses, fuzzy discretization, halving algorithm, single valued total, confident functions Key Phrases - Capitalized Phrases (CAPs): (learn more)
Artificial Neural Networks, Machine Learning Basics, Cluster Analysis, Knowledge Representation, Statistical Learning, Courtelary Le Locle, The Vapnik-Chervonenkis, Insightful Miner, Attribute Quality Measures Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
symbolic learning, data preprocessing, bounded exhaustive search, regressional problems, banner plot, recursive learners, attribute subgraph, learnability paradigm, positive learning examples, negative learning examples, swiss dataset, new binary attributes, query learning model, finite oracle, generalized delta learning rule, statistical query model, original attribute space, independent testing set, random classification noise, weighted majority algorithm, abandoned hypotheses, fuzzy discretization, halving algorithm, single valued total, confident functions Key Phrases - Capitalized Phrases (CAPs): (learn more)
Artificial Neural Networks, Machine Learning Basics, Cluster Analysis, Knowledge Representation, Statistical Learning, Courtelary Le Locle, The Vapnik-Chervonenkis, Insightful Miner, Attribute Quality Measures Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
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