MARC record from Internet Archive

LEADER: 05268cam 2200721Ia 4500
001 ocn828676626
003 OCoLC
005 20220121203403.0
008 130228s2013 enka ob 001 0 eng d
006 m o d
007 cr cnu---unuuu
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019 $a828794661$a839671688
020 $a9781447148845$q(electronic bk.)
020 $a1447148843$q(electronic bk.)
020 $z9781447148838
020 $z1447148835
024 7 $a10.1007/978-1-4471-4884-5$2doi
035 $a(OCoLC)828676626$z(OCoLC)828794661$z(OCoLC)839671688
050 4 $aQA76.9.D343
060 4 $aQA 76.9.D343
082 04 $a006.312$223
084 $a54.64$2bcl
100 1 $aBramer, M. A.$q(Max A.),$d1948-
245 10 $aPrinciples of data mining /$cMax Bramer.
250 $a2nd ed.
260 $aLondon :$bSpringer,$c2013.
300 $a1 online resource
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
490 1 $aUndergraduate topics in computer science
505 00 $tIntroduction to Data Mining --$tData for Data Mining --$tIntroduction to Classification: Naïve Bayes and Nearest Neighbour --$tUsing Decision Trees for Classification --$tDecision Tree Induction: Using Entropy for Attribute Selection --$tDecision Tree Induction: Using Frequency Tables for Attribute Selection --$tEstimating the Predictive Accuracy of a Classifier --$tContinuous Attributes --$tAvoiding Overfitting of Decision Trees --$tMore About Entropy --$tInducing Modular Rules for Classification --$tMeasuring the Performance of a Classifier --$tDealing with Large Volumes of Data --$tEnsemble Classification --$tComparing Classifiers --$tAssociation Rule Mining I --$tAssociation Rule Mining II --$tAssociation Rule Mining III: Frequent Pattern Trees --$tClustering --$tText Mining.
504 $aIncludes bibliographical references and 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.
588 0 $aPrint version record.
650 0 $aData mining.
650 2 $aData Mining
650 7 $aData mining.$2fast$0(OCoLC)fst00887946
655 0 $aElectronic books.
655 4 $aElectronic books.
776 08 $iPrint version:$aBramer, M.A. (Max A.), 1948-$tPrinciples of data mining.$b2nd ed.$dLondon : Springer, 2013$z9781447148838$w(OCoLC)820780629
830 0 $aUndergraduate topics in computer science.
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