| 000 | 02457cam a2200409 a 4500 | ||
|---|---|---|---|
| 008 | 120321s2012 flua b 001 0 eng | ||
| 010 | _a2012008925 | ||
| 015 | _aGBB221015 | ||
| 020 | _a9781466503960 | ||
| 020 | _a1466503963 | ||
| 024 | 8 | _a(YBP)7116456 | |
| 035 | _a(OCoLC)756596227 | ||
| 040 |
_aDLC _beng _cDLC _dYDX _dBTCTA _dUKMGB _dYDXCP _dOCLCO _dBWX _dOCoLC |
||
| 042 | _apcc | ||
| 050 | 0 | 0 |
_aHF5415.126 _b.P88 2012 |
| 082 | 0 | 0 |
_a658.4/0302855133 _223 |
| 089 | 0 | _a658.403028/5 | |
| 100 | 1 | _aPutler, Daniel S. | |
| 245 | 1 | 0 |
_aCustomer and business analytics : _bapplied data mining for business decision making using R / _cDaniel S. Putler, Robert E. Krider. |
| 260 |
_aBoca Raton, FL : _bCRC Press, _cc2012. |
||
| 300 |
_axxvi, 289 p. : _bill. ; _c26 cm. |
||
| 490 | 1 | _aChapman & Hall/CRC the R series | |
| 504 | _aIncludes bibliographical references and index. | ||
| 650 | 0 |
_aDatabase marketing _xSoftware. |
|
| 650 | 0 | _aData mining. | |
| 650 | 0 |
_aDecision making _xData processing. |
|
| 650 | 0 | _aR (Computer program language) | |
| 650 | 0 | _aDatabase management. | |
| 700 | 1 | _aKrider, Robert E. | |
| 830 | 0 | _aChapman & Hall/CRC the R series. | |
| 035 | _a(AuCNLKIN)000048926043 | ||
| 520 | _aCustomer and Business Analytics: Applied Data Mining for Business Decision Making Using R explains and demonstrates, via the accompanying open-source software, how advanced analytical tools can address various business problems. It also gives insight into some of the challenges faced when deploying these tools. Extensively classroom-tested, the text is ideal for students in customer and business analytics or applied data mining as well as professionals in small- to medium-sized organizations. The book offers an intuitive understanding of how different analytics algorithms work. Where necessary, the authors explain the underlying mathematics in an accessible manner. Each technique presented includes a detailed tutorial that enables hands-on experience with real data. The authors also discuss issues often encountered in applied data mining projects and present the CRISP-DM process model as a practical framework for organizing these projects. Showing how data mining can improve the performance of organizations, this book and its R-based software provide the skills and tools needed to successfully develop advanced analytics capabilities. | ||
| 005 | 20170126100456.0 | ||
| 001 | 57663 | ||
| 003 | UOWD | ||
| 942 | _cREGULAR | ||
| 999 |
_c24266 _d24266 |
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