000			03577nam a2200577 i 4500
001			11231819
003			CaPaEBR
005			20241023114848.0
006			m eo d
007			cr cn |||m|||a
008			160715s2016 nyua foab 001 0 eng d
020			_a9781631575495 _qe-book
035			_a(BEP)4571754
035			_a(OCoLC)952663223
035			_a(CaBNVSL)swl00406726
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQA76.9.D343
100	1		_aOlson, David L., _d1944-, _eauthor.
245	1	0	_aData mining models / _cDavid L. Olson.
250			_aFirst edition.
264		1	_aNew York, New York (222 East 46th Street, New York, NY 10017) : _bBusiness Expert Press, _c[(c)2016.]
300			_a1 online resource (172 pages) : _billustrations.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_adata file _2rda
490	1		_aBig data and business analytics collection, _x2333-6757
504			_aIncludes bibliographical references (pages 167-168) and index.
505	0		_a1. Data mining in business -- _t2. Business data mining tools -- _t3. Data mining processes and knowledge discovery -- _t4. Overview of data mining techniques -- _t5. Data mining software -- _t6. Regression algorithms in data mining -- _t7. Neural networks in data mining -- _t8. Decision tree algorithms -- _t9. Scalability -- _tNotes -- _tReferences -- _tIndex.
506			_aAccess restricted to authorized users and institutions.
520	3		_aData mining has become the fastest growing topic of interest in business programs in the past decade. This book is intended to first describe the benefits of data mining in business, describe the process and typical business applications, describe the workings of basic data mining models, and demonstrate each with widely available free software. The book focuses on demonstrating common business data mining applications. It provides exposure to the data mining process, to include problem identification, data management, and available modeling tools. The book takes the approach of demonstrating typical business data sets with open source software. KNIME is a very easy-to-use tool, and is used as the primary means of demonstration. R is much more powerful and is a commercially viable data mining tool. We will demonstrate use of R through Rattle. We also demonstrate WEKA, which is a highly useful academic software, although it is difficult to manipulate test sets and new cases, making it problematic for commercial use. We will demonstrate methods with a small but typical business dataset. We use a larger (but still small) realistic business dataset for Chapter 9.
530			_a2 _ub
530			_aAlso available in printing.
538			_aMode of access: World Wide Web.
538			_aSystem requirements: Adobe Acrobat reader.
588			_aTitle from PDF title page (viewed on July 15, 2016).
650		0	_aData mining.
650		0	_aBusiness _xData processing.
653			_abig data
653			_abusiness analytics
653			_aclustering
653			_adata mining
653			_adecision trees
653			_aneural network models
653			_aregression models
655		0	_a[genre]
776	0	8	_iPrint version: _z9781631575488
830		0	_aBig data and business analytics collection. _x2333-6757
856	4	0	_uhttps://go.openathens.net/redirector/ciu.edu?url=https://portal.igpublish.com/iglibrary/search/BEPB0000538.html
942			_2lcc _bCIU _cOB _eBEP _QOL _zBEP11231819
999			_c73587 _d73587
902			_c1 _dCynthia Snell