000 04374nam a22005415i 4500
001 978-1-4471-7307-6
003 DE-He213
005 20200421112225.0
007 cr nn 008mamaa
008 161109s2016 xxk| s |||| 0|eng d
020 _a9781447173076
_9978-1-4471-7307-6
024 7 _a10.1007/978-1-4471-7307-6
_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 _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: 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 _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 _uhttp://dx.doi.org/10.1007/978-1-4471-7307-6
912 _aZDB-2-SCS
942 _cEBK
999 _c57642
_d57642