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008			161109s2016 xxk| s |||| 0|eng d
020			_a9781447173076
024	7		_a10.1007/978-1-4471-7307-6 _2doi
040			_aES-MaUEC
050		4	_aQA76.9.D343 _bB736 2016 EB
100	1		_aBramer, Max _942045
245	1	0	_aPrinciples of Data Mining _cby Max Bramer
250			_a3rd ed. 2016
260			_aLondon _bSpringer _c2016
300			_a1 recurso en línea (XV, 526 p.) _b123 ilustraciones
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
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.
942			_2lcc _cLE
988			_aEBOOK, EBSPRINGER
650		7	_aData mining _0comprobar BNE20033218554 _2embne _9162648
830		0	_aUndergraduate Topics in Computer Science _x1863-7310 _0http://id.loc.gov/authorities/names/no2007043167 _9134150
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-1-4471-7307-6zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
907			_a.b12980821 _b10-10-17 _c08-03-17
998			_am _a_alco _a_vill _b10-03-17 _cm _dz _ea _feng _gxxk _h0
945			_aQA76.9.D343 B736 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11602806 _z06-04-17
999			_c88092 _d88092 _x1