| 000 | 03453pam a2200193 a 4500 | ||
|---|---|---|---|
| 008 | 140223b2011 xxu||||| |||| 00| 0 eng d | ||
| 020 | _a9781439810187 | ||
| 082 | _a006.312 | ||
| 100 | _aTorgo, Luis | ||
| 245 |
_aData Mining with R : learning with case studies _b |
||
| 250 | _a1st Ed. | ||
| 260 |
_aLondon _c2011 _bCRC Press |
||
| 300 | _axv,289p. | ||
| 365 |
_b52.99 _aGBP |
||
| 650 | _aR (programming language) | ||
| 650 | _aData Mining | ||
| 856 | _uhttp://www.crcpress.com/product/isbn/9781439810187 | ||
| 906 |
_a22.006.312 _b _c<p style="clear: both;line-height: 16.0px;padding: 0.0px 0.0px 12.0px;margin: 0.0px;" /> <p style="clear: both;line-height: 16.0px;padding: 0.0px 0.0px 12.0px;margin: 0.0px;color: rgb(0,0,0);font-family: Verdana , Arial , Helvetica , sans-serif;font-size: 11.0px;font-style: normal;font-variant: normal;font-weight: normal;letter-spacing: normal;text-indent: 0.0px;text-transform: none;white-space: normal;word-spacing: 0.0px;">The versatile capabilities and large set of add-on packages make R an excellent alternative to many existing and often expensive data mining tools. Exploring this area from the perspective of a practitioner, <strong>Data Mining with R: Learning with Case Studies</strong> uses practical examples to illustrate the power of R and data mining.</p> <p style="clear: both;line-height: 16.0px;padding: 0.0px 0.0px 12.0px;margin: 0.0px;color: rgb(0,0,0);font-family: Verdana , Arial , Helvetica , sans-serif;font-size: 11.0px;font-style: normal;font-variant: normal;font-weight: normal;letter-spacing: normal;text-indent: 0.0px;text-transform: none;white-space: normal;word-spacing: 0.0px;"> </p> <p style="clear: both;line-height: 16.0px;padding: 0.0px 0.0px 12.0px;margin: 0.0px;color: rgb(0,0,0);font-family: Verdana , Arial , Helvetica , sans-serif;font-size: 11.0px;font-style: normal;font-variant: normal;font-weight: normal;letter-spacing: normal;text-indent: 0.0px;text-transform: none;white-space: normal;word-spacing: 0.0px;">Assuming no prior knowledge of R or data mining/statistical techniques, the book covers a diverse set of problems that pose different challenges in terms of size, type of data, goals of analysis, and analytical tools. To present the main data mining processes and techniques, the author takes a hands-on approach that utilizes a series of detailed, real-world case studies:</p> <ol style="color: rgb(0,0,0);font-family: Verdana , Arial , Helvetica , sans-serif;font-size: 11.0px;font-style: normal;font-variant: normal;font-weight: normal;letter-spacing: normal;line-height: 16.0px;text-indent: 0.0px;text-transform: none;white-space: normal;word-spacing: 0.0px;"> <li style="padding: 0.0px;margin-left: 7.0px;">Predicting algae blooms</li> <li style="padding: 0.0px;margin-left: 7.0px;">Predicting stock market returns</li> <li style="padding: 0.0px;margin-left: 7.0px;">Detecting fraudulent transactions</li> <li style="padding: 0.0px;margin-left: 7.0px;">Classifying microarray samples</li></ol> <p style="clear: both;line-height: 16.0px;padding: 0.0px 0.0px 12.0px;margin: 0.0px;color: rgb(0,0,0);font-family: Verdana , Arial , Helvetica , sans-serif;font-size: 11.0px;font-style: normal;font-variant: normal;font-weight: normal;letter-spacing: normal;text-indent: 0.0px;text-transform: none;white-space: normal;word-spacing: 0.0px;">With these case studies, the author supplies all necessary st |
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