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Principles of Data Mining
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London :
Springer London : Imprint: Springer,
2007.
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| Edición: | 1st ed. 2007. |
| Colección: | Undergraduate Topics in Computer Science,
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| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 100 | 1 | |a Bramer, Max. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Principles of Data Mining |h [electronic resource] / |c by Max Bramer. |
| 250 | |a 1st ed. 2007. | ||
| 264 | 1 | |a London : |b Springer London : |b Imprint: Springer, |c 2007. | |
| 300 | |a X, 344 p. 200 illus. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 490 | 1 | |a Undergraduate Topics in Computer Science, |x 2197-1781 | |
| 505 | 0 | |a Data for Data Mining -- to Classification: Na¨ive Bayes and Nearest Neighbour -- Using Decision Trees for Classification -- Decision Tree Induction: Using Entropy for Attribute Selection -- Decision Tree Induction: Using Frequency Tables for Attribute Selection -- Estimating the Predictive Accuracy of a Classifier -- Continuous Attributes -- Avoiding Overfitting of Decision Trees -- More About Entropy -- Inducing Modular Rules for Classification -- Measuring the Performance of a Classifier -- Association Rule Mining I -- Association Rule Mining II -- Clustering -- Text Mining. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Data structures (Computer science). | |
| 650 | 0 | |a Information theory. | |
| 650 | 0 | |a Computer science. | |
| 650 | 0 | |a Information storage and retrieval systems. | |
| 650 | 0 | |a Database management. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Computer programming. | |
| 650 | 1 | 4 | |a Data Structures and Information Theory. |
| 650 | 2 | 4 | |a Theory of Computation. |
| 650 | 2 | 4 | |a Information Storage and Retrieval. |
| 650 | 2 | 4 | |a Database Management. |
| 650 | 2 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Programming Techniques. |
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| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9781848006034 |
| 776 | 0 | 8 | |i Printed edition: |z 9781846287657 |
| 830 | 0 | |a Undergraduate Topics in Computer Science, |x 2197-1781 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-1-84628-766-4 |z Texto Completo |
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| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||