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001			978-1-4471-7307-6
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020			_a9781447173076 _9978-1-4471-7307-6
050		4	_aQA75.5-76.95
072		7	_aUNH _2bicssc
072		7	_aUND _2bicssc
072		7	_aCOM030000 _2bisacsh
082	0	4	_a025.04 _223
100	1		_aBramer, Max. _eauthor.
245	1	0	_aPrinciples of Data Mining _h[recurso electrónico] / _cby Max Bramer.
250			_a3rd ed. 2016.
264		1	_aLondon : _bSpringer London : _bImprint: Springer, _c2016.
300			_aXV, 526 p. 123 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aUndergraduate Topics in Computer Science, _x1863-7310
505	0		_aIntroduction to Data Mining -- Data for Data Mining -- Introduction to Classification: Naïve 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			_aThis 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.
650		0	_aComputer science.
650		0	_aComputer programming.
650		0	_aDatabase management.
650		0	_aInformation storage and retrieval.
650		0	_aArtificial intelligence.
650	1	4	_aComputer Science.
650	2	4	_aInformation Storage and Retrieval.
650	2	4	_aDatabase Management.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aProgramming Techniques.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781447173069
830		0	_aUndergraduate Topics in Computer Science, _x1863-7310
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://dx.doi.org/10.1007/978-1-4471-7307-6
912			_aZDB-2-SCS
942			_cLIBRO_ELEC
999			_c225870 _d225870