Principles of Data Mining (Record no. 57642)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 04374nam a22005415i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-1-4471-7307-6 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20200421112225.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 161109s2016 xxk| s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781447173076 |
| -- | 978-1-4471-7307-6 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 025.04 |
| 100 1# - AUTHOR NAME | |
| Author | Bramer, Max. |
| 245 10 - TITLE STATEMENT | |
| Title | Principles of Data Mining |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 3rd ed. 2016. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XV, 526 p. 123 illus. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Undergraduate Topics in Computer Science, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Introduction to Data Mining -- Data for Data Mining -- Introduction 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 -- Dealing with Large Volumes of Data -- Ensemble Classification -- Comparing Classifiers -- Associate Rule Mining I -- Associate Rule Mining II -- Associate Rule Mining III -- Clustering -- Mining -- Classifying Streaming Data -- Classifying Streaming Data II: Time-dependent Data -- Appendix A - Essential Mathematics -- Appendix B - Datasets -- Appendix C - Sources of Further Information -- Appendix D - Glossary and Notation -- Appendix E - Solutions to Self-assessment Exercises -- Index. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering. Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail. 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 general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers 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. This expanded third edition includes detailed descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | http://dx.doi.org/10.1007/978-1-4471-7307-6 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | London : |
| -- | Springer London : |
| -- | Imprint: Springer, |
| -- | 2016. |
| 336 ## - | |
| -- | text |
| -- | txt |
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| -- | computer |
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| -- | online resource |
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| 347 ## - | |
| -- | text file |
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| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer science. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer programming. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Database management. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Information storage and retrieval. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer Science. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Information Storage and Retrieval. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Database Management. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial Intelligence (incl. Robotics). |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Programming Techniques. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 1863-7310 |
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