LEADER 00000cam a2200649 a 4500 
001    699810033 
003    OCoLC 
005    20240129213017.0 
006    m     o  d         
007    cr unu|||||||| 
008    110201s2010    njua    ob    001 0 eng d 
019    846860757 
020    |q(cloth) 
020    |q(cloth) 
029 1  DEBSZ|b355416778 
029 1  GBVCP|b785355235 
029 1  HEBIS|b291488773 
029 1  NZ1|b14926889 
035    (OCoLC)699810033|z(OCoLC)846860757 
037    CL0500000082|bSafari Books Online 
040    UMI|beng|epn|cUMI|dOCLCQ|dDEBSZ|dYDXCP|dOCLCQ|dVT2|dOCLCF
       |dOCLCQ|dUPM|dTEFOD|dOCLCQ|dZ5A|dOCLCQ|dOCLCO|dCEF|dAU@
       |dOCLCQ|dUAB|dOCLCO|dOCLCQ|dOCLCO|dOCL|dOCLCO|dOCLCL
       |dOCLCQ|dOCLCL 
049    INap 
082 04 005.54 
082 04 005.54|222 
099    eBook O’Reilly for Public Libraries 
100 1  Shmueli, Galit,|d1971-|1https://id.oclc.org/worldcat/
       entity/E39PBJgmKrF6yhWBmTPkmRw6Kd 
245 10 Data mining for business intelligence :|bconcepts, 
       techniques, and applications in Microsoft Office Excel 
       with XLMiner /|cGalit Shmueli, Nitin R. Patel, Peter C. 
       Bruce.|h[O'Reilly electronic resource] 
250    2nd ed. 
260    Hoboken, N.J. :|bWiley,|c©2010. 
300    1 online resource (xxiv, 404 pages) :|billustrations 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
504    Includes bibliographical references and index. 
505 0  Introduction -- Overview of the data mining process -- 
       Data visualization -- Dimension reduction -- Evaluating 
       classification and predictive performance -- Multiple 
       linear regression -- knearest neighbors (kNN) -- Naive 
       bayes -- Classification and regression trees -- Logistic 
       regression -- Neural nets -- Discriminant analysis -- 
       Association rules -- Cluster Analysis -- Handling time 
       series -- Regression-based forecasting -- Smoothing 
       methods -- Cases. 
520    Data Mining for Business Intelligence, Second Edition uses
       real data and actual cases to illustrate the applicability
       of data mining (DM) intelligence in the development of 
       successful business models. Featuring complimentary access
       to XLMiner, the Microsoft Office Excel add-in, this book 
       allows readers to follow along and implement algorithms at
       their own speed, with a minimal learning curve. In 
       addition, students and practitioners of DM techniques are 
       presented with hands-on, business-oriented applications. 
       An abundant amount of exercises and examples, now doubled 
       in number in the second edit. 
588 0  Print version record. 
590    O'Reilly|bO'Reilly Online Learning: Academic/Public 
       Library Edition 
630 00 Microsoft Excel (Computer file) 
630 07 Microsoft Excel (Computer file)|2blmlsh 
630 07 Microsoft Excel (Computer file)|2fast 
650  0 Business|xData processing. 
650  0 Data mining. 
650  0 Management|xData processing. 
650  6 Gestion|xInformatique. 
650  6 Exploration de données (Informatique) 
650  7 Business|xData processing.|2blmlsh 
650  7 Data mining.|2blmlsh 
650  7 Management|xData processing|2fast 
650  7 Business|xData processing|2fast 
650  7 Data mining|2fast 
700 1  Patel, Nitin R.|q(Nitin Ratilal)|1https://id.oclc.org/
       worldcat/entity/E39PCjwfwxkVkqYywxC7kbfd6X 
700 1  Bruce, Peter C.,|d1953-|1https://id.oclc.org/worldcat/
       entity/E39PCjKjRYw784cPyqy7C6KVBX 
776 08 |iPrint version:|aShmueli, Galit, 1971-|tData mining for 
       business intelligence.|b2nd ed.|dHoboken, N.J. : Wiley, 
       ©2010|z9780470526828|w(DLC)  2010005152|w(OCoLC)531718699 
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
       learning.oreilly.com/library/view/~/9780470526828/?ar
       |zAvailable on O'Reilly for Public Libraries 
938    YBP Library Services|bYANK|n9659636 
994    92|bJFN