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020 _a9781447148845
_9978-1-4471-4884-5
024 7 _a10.1007/978-1-4471-4884-5
_2doi
035 _ato000483465
040 _aSpringer
_cSpringer
_dRU-ToGU
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.
_9307580
245 1 0 _aPrinciples of Data Mining
_helectronic resource
_cby Max Bramer.
250 _a2nd ed. 2013.
260 _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
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.
_9155490
650 0 _aDatabase management.
_9566224
650 0 _aInformation storage and retrieval systems.
_9137013
650 0 _aArtificial intelligence.
_9274099
650 1 4 _aComputer Science.
_9155490
650 2 4 _aInformation Storage and Retrieval.
_9303027
650 2 4 _aDatabase Management.
_9566226
650 2 4 _aArtificial Intelligence (incl. Robotics).
_9274102
650 2 4 _aProgramming Techniques.
_9566312
710 2 _aSpringerLink (Online service)
_9143950
773 0 _tSpringer eBooks
830 0 _aUndergraduate Topics in Computer Science,
_9318115
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4471-4884-5
912 _aZDB-2-SCS
999 _c356174