000			04126nam a22005417a 4500
001			sulb-eb0021591
003			BD-SySUS
005			20160413122150.0
007			cr nn 008mamaa
008			130220s2013 xxk| s |||| 0|eng d
020			_a9781447148845 _9978-1-4471-4884-5
024	7		_a10.1007/978-1-4471-4884-5 _2doi
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[electronic resource] / _cby Max Bramer.
250			_a2nd ed. 2013.
264		1	_aLondon : _bSpringer London : _bImprint: Springer, _c2013.
300			_aXIV, 440 p. 101 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 -- 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			_aData Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas. Principles of Data Mining explains and explores the principal techniques of Data Mining: for classification, association rule mining and clustering. Each topic is clearly explained and illustrated by detailed worked examples, with a focus on algorithms rather than mathematical formalism. It is written for readers without a strong background in mathematics or statistics, and any formulae used are explained in detail. This second edition has been expanded to include additional chapters on using frequent pattern trees for Association Rule Mining, comparing classifiers, ensemble classification and dealing with very large volumes of data. Principles of Data Mining 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. Suitable 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.
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: _z9781447148838
830		0	_aUndergraduate Topics in Computer Science, _x1863-7310
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4471-4884-5
912			_aZDB-2-SCS
942			_2Dewey Decimal Classification _ceBooks
999			_c43683 _d43683