| 000 | 04778nam a2200913 i 4500 | ||
|---|---|---|---|
| 001 | 5201921 | ||
| 003 | IEEE | ||
| 005 | 20200421114109.0 | ||
| 006 | m o d | ||
| 007 | cr |n||||||||| | ||
| 008 | 151221s2006 njua ob 001 eng d | ||
| 020 |
_a9780471756484 _qelectronic |
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| 020 |
_z9780471666561 _qprint |
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| 020 |
_z0471756482 _qelectronic |
||
| 024 | 7 |
_a10.1002/0471756482 _2doi |
|
| 035 | _a(CaBNVSL)mat05201921 | ||
| 035 | _a(IDAMS)0b0000648104aefb | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
||
| 050 | 4 |
_aQA76.9.D343 _bL378 2006eb |
|
| 082 | 0 | 4 |
_a005.74 _222 |
| 100 | 1 |
_aLarose, Daniel T., _eauthor. |
|
| 245 | 1 | 0 |
_aData mining methods and models / _cDaniel T. Larose. |
| 264 | 1 |
_aHoboken, New Jersey : _bWiley-Interscience, _cc2006. |
|
| 264 | 2 |
_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2006] |
|
| 300 |
_a1 PDF (xvi, 322 pages) : _billustrations. |
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| 336 |
_atext _2rdacontent |
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| 337 |
_aelectronic _2isbdmedia |
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| 338 |
_aonline resource _2rdacarrier |
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| 504 | _aIncludes bibliographical references and index. | ||
| 505 | 0 | _aDimension reduction methods -- Regression modeling -- Multiple regression and model building -- Logistic regression -- Na�ive Bayes estimation and Bayesian networks -- Genetic algorithms -- Case study : modeling response to direct mail marketing. | |
| 506 | 1 | _aRestricted to subscribers or individual electronic text purchasers. | |
| 520 | _aApply powerful Data Mining Methods and Models to Leverage your Data for Actionable Results Data Mining Methods and Models provides: * The latest techniques for uncovering hidden nuggets of information * The insight into how the data mining algorithms actually work * The hands-on experience of performing data mining on large data sets Data Mining Methods and Models: * Applies a "white box" methodology, emphasizing an understanding of the model structures underlying the softwareWalks the reader through the various algorithms and provides examples of the operation of the algorithms on actual large data sets, including a detailed case study, "Modeling Response to Direct-Mail Marketing" * Tests the reader's level of understanding of the concepts and methodologies, with over 110 chapter exercises * Demonstrates the Clementine data mining software suite, WEKA open source data mining software, SPSS statistical software, and Minitab statistical software * Includes a companion Web site, www.dataminingconsultant.com, where the data sets used in the book may be downloaded, along with a comprehensive set of data mining resources. Faculty adopters of the book have access to an array of helpful resources, including solutions to all exercises, a PowerPoint(r) presentation of each chapter, sample data mining course projects and accompanying data sets, and multiple-choice chapter quizzes. With its emphasis on learning by doing, this is an excellent textbook for students in business, computer science, and statistics, as well as a problem-solving reference for data analysts and professionals in the field. An Instructor's Manual presenting detailed solutions to all the problems in the book is available onlne. | ||
| 530 | _aAlso available in print. | ||
| 538 | _aMode of access: World Wide Web | ||
| 588 | _aDescription based on PDF viewed 12/21/2015. | ||
| 650 | 0 | _aData mining. | |
| 655 | 0 | _aElectronic books. | |
| 695 | _aAdaptation model | ||
| 695 | _aAnalytical models | ||
| 695 | _aApproximation methods | ||
| 695 | _aBayesian methods | ||
| 695 | _aBiological cells | ||
| 695 | _aBusiness | ||
| 695 | _aCloning | ||
| 695 | _aClothing | ||
| 695 | _aCompanies | ||
| 695 | _aComputational modeling | ||
| 695 | _aComputer aided software engineering | ||
| 695 | _aCorrelation | ||
| 695 | _aCovariance matrix | ||
| 695 | _aDNA | ||
| 695 | _aData mining | ||
| 695 | _aData models | ||
| 695 | _aData visualization | ||
| 695 | _aDiseases | ||
| 695 | _aEigenvalues and eigenfunctions | ||
| 695 | _aEquations | ||
| 695 | _aEstimation | ||
| 695 | _aGallium | ||
| 695 | _aGenetic algorithms | ||
| 695 | _aIndexes | ||
| 695 | _aLeast squares approximation | ||
| 695 | _aLinear regression | ||
| 695 | _aLogistics | ||
| 695 | _aMathematical model | ||
| 695 | _aMatrices | ||
| 695 | _aMaximum likelihood estimation | ||
| 695 | _aPostal services | ||
| 695 | _aPredictive models | ||
| 695 | _aPrincipal component analysis | ||
| 695 | _aProteins | ||
| 695 | _aSugar | ||
| 695 | _aWheels | ||
| 710 | 2 |
_aJohn Wiley & Sons, _epublisher. |
|
| 710 | 2 |
_aIEEE Xplore (Online service), _edistributor. |
|
| 776 | 0 | 8 |
_iPrint version: _z9780471666561 |
| 856 | 4 | 2 |
_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=5201921 |
| 942 | _cEBK | ||
| 999 |
_c59285 _d59285 |
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