This book explains the principal techniques of data mining: for classification, generation of association rules and clustering. It is written for readers without a strong background in mathematics or statistics and focuses on detailed examples and explanations of the algorithms given. This will benefit readers of all levels, from those who use data mining via commercial packages, right through to academic researchers. The book aims to help the general reader develop the necessary understanding to use commercial data mining packages, and to enable advanced readers to understand or contribute to future technical advances. Includes exercises and glossary.
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Data Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas. This book explains and explores the principal techniques of Data Mining: for classification, generation of association rules and clustering. It is written for readers without a strong background in mathematics or statistics and focuses on detailed examples & explanations of the algorithms given. It can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science. As an aid to self study, this book aims to help the general reader develop the necessary understanding to use commercial data mining packages discriminatingly, as well as enabling the advanced reader or academic researcher to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included.
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2 of 2 people found the following review helpful:
5.0 out of 5 stars
Excellent coverage & depth,
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This review is from: Principles of Data Mining (Undergraduate Topics in Computer Science) (Paperback)
I bought this book for self-study and I am very surprised by the clarity and 'just-nice' amount of depth and coverage on the topic.I pretty much able to understand most of the content by reading the book and wiki on the more difficult topic.
2 of 2 people found the following review helpful:
4.0 out of 5 stars
Introduction,
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This review is from: Principles of Data Mining (Undergraduate Topics in Computer Science) (Paperback)
This is an undergraduate introduction to data mining. The book doesn't go into details. It may be suitable for people who want to get a quick feel of the data mining field. People who need more details shall read more serious and comprehensive introductions. Overall I am giving 4 stars, because I liked it.
3 of 4 people found the following review helpful:
5.0 out of 5 stars
excellent introduction,
By Steve (Denver, CO) - See all my reviews
Amazon Verified Purchase(What's this?)
This review is from: Principles of Data Mining (Undergraduate Topics in Computer Science) (Paperback)
This book is an excellent introduction to data mining, concentrating primarily on decision tree induction. The material provided is presented clearly with no assumption of prior knowledge on the part of the reader. A weakness of the book is that it doesn't place the material provided within the larger context of machine learning, both in terms of breadth or depth. However, when used as a textbook the instructor could easily address this problem.
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Inside This Book
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Key Phrases - Statistically Improbable Phrases (SIPs):
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association rule mining, repository classes, text mining, decision tree induction, market basket analysis, dataset description, agglomerative hierarchical clustering, hypertext classification, static error rate, initial frequency table, clash threshold, global discretisation, supported itemsets, local discretisation, hypertext categorisation, genetics dataset, candidate cut points, rule interestingness measures, chess dataset, tears class, information gain values, modular rules, generating classification rules, unseen instances, unclassified instances Key Phrases - Capitalized Phrases (CAPs): (learn more)
Principles of Data Mining, Gain Ratio, Instances Training, Self-assessment Exercises, Gini Index, More About Entropy, Avoiding Overfitting of Decision Trees, Essential Mathematics, Probability of Attribute, Second Rule, Nearest Neighbour, World Wide Web, Naïve Bayes, Rules Test, Rate Figure, Machine Learning, First Rule, Correct Incorrect, Project Class, Expert Systems, High Street, Description Monk's Problem, Average Value New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
association rule mining, repository classes, text mining, decision tree induction, market basket analysis, dataset description, agglomerative hierarchical clustering, hypertext classification, static error rate, initial frequency table, clash threshold, global discretisation, supported itemsets, local discretisation, hypertext categorisation, genetics dataset, candidate cut points, rule interestingness measures, chess dataset, tears class, information gain values, modular rules, generating classification rules, unseen instances, unclassified instances Key Phrases - Capitalized Phrases (CAPs): (learn more)
Principles of Data Mining, Gain Ratio, Instances Training, Self-assessment Exercises, Gini Index, More About Entropy, Avoiding Overfitting of Decision Trees, Essential Mathematics, Probability of Attribute, Second Rule, Nearest Neighbour, World Wide Web, Naïve Bayes, Rules Test, Rate Figure, Machine Learning, First Rule, Correct Incorrect, Project Class, Expert Systems, High Street, Description Monk's Problem, Average Value New!
Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
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