000			03662nam a2200421 i 4500
005			20250919002746.0
008			150317s2011 flua b 001 0 eng
020			_a9781439810187 _q(hardback) _cRM320.10
020			_a1439810184 _q(hardback)
039		9	_a201509021650 _basrul _c201508240933 _drosli _c201508051435 _dbinar _c201503171209 _dbinar _y03-17-2015 _zbinar
040			_aDLC _beng _cDLC _dYDX _dYDXCP _dCDX _dBWX _dUKMGB _dRRR _dOCLCF _dMNW _dOCLCQ _dCRCPR _dOCLCQ _dUKM _erda
090			_aQA76.9.D343T637
090			_aQA76.9.D343 _bT637
100	1		_aTorgo, Luis, _eauthor.
245	1	0	_aData mining with R : _blearning with case studies / _cLu Torgo.
264		1	_aBoca Raton : _bChapman & Hall/CRC, _cò011.
264		4	_c©2011.
300			_axv, 289 pages : _billustrations ; _c25 cm.
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
338			_avolume _2rdacarrier
490	1		_aChapman & Hall/CRC data mining and knowledge discovery series
504			_aIncludes bibliographical references (pages 269-277) and indexes.
505	0		_aIntroduction -- Predicting algae blooms -- Predicting stock market returns -- Detecting fraudulent transactions -- Classifying microarray samples.
520			_a'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, Data mining with R: learning with case studies uses practical examples to illustrate the power of R and data mining. 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: predicting algae blooms, predicting stock market returns, detecting fraudulent transactions, classifying microarray samples. With these case studies, the author supplies all necessary steps, code, and data. Resource: A supporting website mirrors the do-it-yourself approach of the text. It offers a collection of freely available R source files that encompass all the code used in the case studies. The site also provides the data sets from the case studies as well as an R package of several functions'-- _cProvided by publisher.
520			_a'This hands-on book uses practical examples to illustrate the power of R and data mining. Assuming no prior knowledge of R or data mining/statistical techniques, it covers a diverse set of problems that pose different challenges in terms of size, type of data, goals of analysis, and analytical tools. The main data mining processes and techniques are presented through detailed, real-world case studies. With these case studies, the author supplies all necessary steps, code, and data. Mirroring the do-it-yourself approach of the text, the supporting website provides data sets and R code'-- _cProvided by publisher.
650		0	_aData mining _vCase studies.
650		0	_aR (Computer program language).
830		0	_aChapman & Hall/CRC data mining and knowledge discovery series.
907			_a.b16096538 _b2019-11-12 _c2019-11-12
942			_c01 _n0 _kQA76.9.D343T637
914			_avtls003581148
990			_ark4
991			_aFakulti Sains dan Teknologi
998			_at _b2015-04-03 _cm _da _feng _gflu _y0 _z.b16096538
999			_c588717 _d588717