000			04402nam a22003497a 4500
003			OSt
005			20230213104850.0
008			230127b xxu||||| |||| 00| 0 eng d
020			_a9780124166325 [hardbound]
040			_aUniversity of Cebu-Banilad _cUniversity of Cebu-Banilad
082			_a006.312015195 N63 2018
100			_aNisbet, Robert, _eauthor.
245			_aHandbook of statistical analysis and data mining applications / _cRobert Nisbet, Gary Miner, Ken Yale ; guest authors of selected chapters, John Elder, Andy Peterson.
250			_aSecond edition.
260			_aAmsterdam : _bAcademic Press, _cc2018.
300			_axxix, 792 pages : _bcolor illustrations, charts (chiefly color), portraits (some color) ; _c25 cm
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
504			_aIncludes bibliographical references and index.
505			_aContents: Part I History of phase of data analysis, basic theory, and the data mining process -- 1 The background for data mining practice -- 2 Theoretical considerations for data mining -- 3 The data mining and predictive analytic process -- 4 Data understanding and preparation -- 5 Feature selection -- 6 Accessory tools for doing data mining -- Part II The algorithms and methods in data mining and predictive analytics and some domain areas -- 7 Basic algorithms for data mining: a brief overview -- 8 Advanced algorithms for data mining -- 9 Classification -- 10 Numerical prediction -- 11 Model evaluation and enhancement -- 12 Predictive analytics for population health and care -- 13 Big data in education: new efficiencies for recruitment, learning and retention of students and donors -- 14 Customer response modeling -- 15 Fraud detention -- Part III Tutorials and case studies -- Tutorial A Example of data mining recipes using windows 10 and statistica 13 -- Tutorial B Using the statistica data mining workspace method for analysis of hurricane data (Hurrdata.sta) -- Tutorial C Case study - using SPSS modeler and STATISTICA to predict student success at high-stakes nuring examination (NCLEX) -- Tutorial D Constructing a histogram in KNIME using MidWest company personality data -- Tutorial E Feature selection on KNIME -- Tutorial F Medical/business tutorial -- Tutorial G A KNIME exercise, using alzheimer's training data ot tutorial F -- Tutorial H Data prep 1-1: merging data sources -- Tutorial I Data prep1-2: data description -- Tutorial J Data prep 2-1: data cleaning and recording -- Tutorial K Data prep 2-2: dummy coding category variables -- Tutorial L Data prep 2-3 Outlier handling -- Tutorial M Data prep 3-1: filling missing values with constrants -- Tutorial N Data prep 3-2: filling missing values with formulas -- Tutorial O Data prep 3-3: filling missing values with a model -- Tutorial P City of Chicago crime map: a case study predicting certain kinds of crime using statistica data miner and text miner -- Tutorial Q Using customer churn data to develop and select a best predictive model for client defection using STATISTICA data miner 13 64-bit for windows 10 -- Tutorial R Example with C&RT to predict and display possible structural relationships -- Tutorial S Clinical psychology: making decisions about best therapy for a client -- Part IV Model ensembles, model complexity; using the right model for the right use, significance, ethics, and the future, and advanced processes -- 16 The apparent paradox of complexity in ensemble modeling -- 17 The "right model" for the "right purpose": when less is good enough -- 18 A data preparation cookbook -- 19 Deep learning -- 20 Signigicance versus luck in the age of mining: the issues of P-value "significance" and "ways to test significance of our predictive analytic models" -- 21 Ethics and data analytics -- 22 IBM watson.
541			_xMoma Ortega _yComputer Studies, Information Technology, Information Systems and Animation _zComputer Studies : Information Technology
546			_aEnglish
650			_aData mining _xStatistical methods.
650			_aStatistics.
700			_aMiner, Gary, _eauthor.
700			_aYale, Ken, _eauthor.
700			_aElder, John F. _q(John Fletcher), _ewriter of supplementary textual content.
700			_aPeterson, Andrew F., _d1960- _ewriter of supplementary textual content.
942			_2ddc _cBK
998			_cAillen[new] _d01/27/2023
999			_c11309 _d11309