This comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribes the sequence in which data mining projects should be performed, from problem and data understanding through data preprocessing to deployment of the results. This knowledge discovery approach is what distinguishes Data Mining from other texts in this area. The book provides a suite of exercises and includes links to instructional presentations. Furthermore, it contains appendices of relevant mathematical material.
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From the reviews: “This is a comprehensive book about knowledge discovery methods. … the book is highly recommended to final year undergraduate students, postgraduate students and lecturers. … it has a good balance of various topics making it a good reference book for practitioners, such as data modellers, insight analysts, fraud analysts, etc., as well as researchers. … this book is very well organized and presented. … I would certainly recommend it to those with intermediate or advanced understanding of data-mining topics.” (Boran Gazi, The Computer Journal, Vol. 53 (4), 2010)
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This comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribe the sequence in which data mining projects should be performed. Data Mining offers an authoritative treatment of all development phases from problem and data understanding through data preprocessing to deployment of the results. This knowledge discovery approach is what distinguishes this book from other texts in the area. It concentrates on data preparation, clustering and association rule learning (required for processing unsupervised data), decision trees, rule induction algorithms, neural networks, and many other data mining methods, focusing predominantly on those which have proven successful in data mining projects. Based upon the authors’ previous successful book on data mining and knowledge discovery, this new volume has been extensively expanded, making it an effective instructional tool for advanced-level undergraduate and graduate courses. This book offers: A suite of exercises at the end of every chapter, designed to enhance the reader’s understanding of the theory and proficiency with the tools presented Links to all-inclusive instructional presentations for each chapter to ensure easy use in classroom teaching Extensive appendices covering relevant mathematical material for convenient look-up Methods for addressing issues related to data privacy and security within the context of data mining, enabling the reader to balance potentially conflicting aims Summaries and bibliographical notes for each chapter, providing a broader perspective of the concepts and methods described Researchers, practitioners and students are certain to consider this volume an indispensable resource in successfully accomplishing the goals of their data mining projects.
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1 of 1 people found the following review helpful:
5.0 out of 5 stars
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This review is from: Data Mining: A Knowledge Discovery Approach (Hardcover)
Book itself is not that well written like Duda book for pattern classification. A little dry but over all it's all good. I am happy about the quality of this book I bought.
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granular computing, independent component analysis, supervised discretization algorithms, split information, minimum description length, dynamic discretization algorithm, knowledge discovery process models, quanta matrix, inductive machine learning algorithms, selector ranking, iith data, razor heuristic, fuzzification coefficient, interclass separability, average distortion measure, feature selection criterion, input pattern space, information granules, most dominant eigenvalue, feature selection criteria, optimal feature selection, simple neuron models, independent component vectors, shadowed sets, chest pain type Key Phrases - Capitalized Phrases (CAPs): (learn more)
Supervised Learning, Statistical Methods, International Conference, Neural Networks, Data Warehouses, Rule Algorithms, Their Hybrids, Feature Extraction, Konrad Black, Springer Verlag, John Wiley, Knowledge Representation, Bayesian Methods, Concepts of Learning, Anna White, Magda Doe, On-Line Analytical Processing, Joe Dalton, Management of Data, Assessment of Data Models, Will Smith, Very Large Databases, Fuzzy Systems, Morgan Kaufmann, Statistical Pattern Recognition Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Surprise Me!
granular computing, independent component analysis, supervised discretization algorithms, split information, minimum description length, dynamic discretization algorithm, knowledge discovery process models, quanta matrix, inductive machine learning algorithms, selector ranking, iith data, razor heuristic, fuzzification coefficient, interclass separability, average distortion measure, feature selection criterion, input pattern space, information granules, most dominant eigenvalue, feature selection criteria, optimal feature selection, simple neuron models, independent component vectors, shadowed sets, chest pain type Key Phrases - Capitalized Phrases (CAPs): (learn more)
Supervised Learning, Statistical Methods, International Conference, Neural Networks, Data Warehouses, Rule Algorithms, Their Hybrids, Feature Extraction, Konrad Black, Springer Verlag, John Wiley, Knowledge Representation, Bayesian Methods, Concepts of Learning, Anna White, Magda Doe, On-Line Analytical Processing, Joe Dalton, Management of Data, Assessment of Data Models, Will Smith, Very Large Databases, Fuzzy Systems, Morgan Kaufmann, Statistical Pattern Recognition Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Surprise Me!
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