LEADER 04454nam 2200997 450 001 9910814373503321 005 20230126211141.0 010 $a0-520-28098-9 010 $a0-520-96059-9 024 7 $a10.1525/9780520960596 035 $a(CKB)2670000000602040 035 $a(EBL)1882080 035 $a(SSID)ssj0001437791 035 $a(PQKBManifestationID)12536720 035 $a(PQKBTitleCode)TC0001437791 035 $a(PQKBWorkID)11373799 035 $a(PQKB)11120303 035 $a(MiAaPQ)EBC1882080 035 $a(DE-B1597)519142 035 $a(OCoLC)905221641 035 $a(DE-B1597)9780520960596 035 $a(Au-PeEL)EBL1882080 035 $a(CaPaEBR)ebr11033069 035 $a(CaONFJC)MIL751724 035 $a(EXLCZ)992670000000602040 100 $a20150328h20152015 uy 0 101 0 $aeng 135 $aur|nu---|u||u 181 $ctxt 182 $cc 183 $acr 200 10$aData mining for the social sciences $ean introduction /$fPaul Attewell and David B. Monaghan 210 1$aOakland, California :$cUniversity of California Press,$d2015. 210 4$dİ2015 215 $a1 online resource (265 p.) 300 $aDescription based upon print version of record. 311 0 $a0-520-28097-0 311 0 $a1-336-20438-9 320 $aIncludes bibliographical references and index. 327 $tFront matter --$tCONTENTS --$tACKNOWLEDGMENTS --$t1. WHAT IS DATA MINING? --$t2. CONTRASTS WITH THE CONVENTIONAL STATISTICAL APPROACH --$t3. SOME GENERAL STRATEGIES USED IN DATA MINING --$t4. IMPORTANT STAGES IN A DATA MINING PROJECT --$t5. PREPARING TRAINING AND TEST DATASETS --$t6. VARIABLE SELECTION TOOLS --$t7. CREATING NEW VARIABLES --$t8. EXTRACTING VARIABLES --$t9. CLASSIFIERS --$t10. CLASSIFICATION TREES --$t11. NEURAL NETWORKS --$t12. CLUSTERING --$t13. LATENT CLASS ANALYSIS AND MIXTURE MODELS --$t14. ASSOCIATION RULES --$tCONCLUSION. Where Next? --$tBIBLIOGRAPHY --$tNOTES --$tINDEX 330 $aWe live in a world of big data: the amount of information collected on human behavior each day is staggering, and exponentially greater than at any time in the past. Additionally, powerful algorithms are capable of churning through seas of data to uncover patterns. Providing a simple and accessible introduction to data mining, Paul Attewell and David B. Monaghan discuss how data mining substantially differs from conventional statistical modeling familiar to most social scientists. The authors also empower social scientists to tap into these new resources and incorporate data mining methodologies in their analytical toolkits. Data Mining for the Social Sciences demystifies the process by describing the diverse set of techniques available, discussing the strengths and weaknesses of various approaches, and giving practical demonstrations of how to carry out analyses using tools in various statistical software packages. 606 $aSocial sciences$xData processing 606 $aSocial sciences$xStatistical methods 606 $aData mining 610 $aanalyzing data. 610 $abayesian networks. 610 $abig data. 610 $abootstrapping. 610 $abusiness analytics. 610 $achaid. 610 $aclassification and regression trees. 610 $aclassification trees. 610 $aconfusion matrix. 610 $adata analysis. 610 $adata mining. 610 $adata processing. 610 $adata scholarship. 610 $adata science. 610 $ahardware for data mining. 610 $aheteroscedasticity. 610 $anaive bayes. 610 $apartition trees. 610 $apermutation tests. 610 $ascholarly data. 610 $asocial science. 610 $asocial scientists. 610 $asoftware for data mining. 610 $astatistical methods. 610 $astatistical modeling. 610 $astudying data. 610 $atext mining. 610 $avif regression. 610 $aweka. 615 0$aSocial sciences$xData processing. 615 0$aSocial sciences$xStatistical methods. 615 0$aData mining. 676 $a006.3/12 700 $aAttewell$b Paul A.$f1949-$01697732 702 $aMonaghan$b David B.$f1988- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910814373503321 996 $aData mining for the social sciences$94078685 997 $aUNINA