000			03689cam a2200517Mi 4500
001			9781003091448
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005			20240213122831.0
006			m o d
007			cr |n|||||||||
008			220820s2022 flu ob 001 0 eng d
040			_aOCoLC-P _beng _cOCoLC-P
020			_a9781000778021 _q(electronic bk.)
020			_a1000778029 _q(electronic bk.)
020			_a9781003091448 _q(electronic bk.)
020			_a100309144X _q(electronic bk.)
020			_a9781000777741 _q(electronic bk. : PDF)
020			_a100077774X _q(electronic bk. : PDF)
020			_z9780367549800
020			_z0367549808
020			_z9780367549824
020			_z0367549824
024	7		_a10.1201/9781003091448 _2doi
035			_a(OCoLC)1341394198
035			_a(OCoLC-P)1341394198
050		4	_aQA76.9.A955
072		7	_aBUS _x061000 _2bisacsh
072		7	_aCOM _x021030 _2bisacsh
072		7	_aMAT _x004000 _2bisacsh
072		7	_aPBCD _2bicssc
082	0	4	_a004.01/5113 _223/eng/20220416
100	1		_aRauch, Jan, _eauthor.
245	1	0	_aMechanizing hypothesis formation _h[electronic resource] : _bprinciples and case studies / _cJan Rauch, Milan Šimůnek, David Chudán, Department of Information and Knowledge Engineering University of Economics, Prague, Czech Republic, Petr Máša.
250			_aFirst edition.
264		1	_aBoca Raton : _bCRC Press ; _bTaylor and Francis Group, _c2022.
300			_a1 online resource
520			_aMechanizing hypothesis formation is an approach to exploratory data analysis. Its development started in the 1960s inspired by the question can computers formulate and verify scientific hypotheses?. The development resulted in a general theory of logic of discovery. It comprises theoretical calculi dealing with theoretical statements as well as observational calculi dealing with observational statements concerning finite results of observation. Both calculi are related through statistical hypotheses tests. A GUHA method is a tool of the logic of discovery. It uses a one-to-one relation between theoretical and observational statements to get all interesting theoretical statements. A GUHA procedure generates all interesting observational statements and verifies them in a given observational data. Output of the procedure consists of all observational statements true in the given data. Several GUHA procedures dealing with association rules, couples of association rules, action rules, histograms, couples of histograms, and patterns based on general contingency tables are involved in the LISp-Miner system developed at the Prague University of Economics and Business. Various results about observational calculi were achieved and applied together with the LISp-Miner system. The book covers a brief overview of logic of discovery. Many examples of applications of the GUHA procedures to solve real problems relevant to data mining and business intelligence are presented. An overview of recent research results relevant to dealing with domain knowledge in data mining and its automation is provided. Firsthand experiences with implementation of the GUHA method in the Python language are presented.
588			_aOCLC-licensed vendor bibliographic record.
650		7	_aBUSINESS & ECONOMICS / Statistics _2bisacsh
650		7	_aCOMPUTERS / Database Management / Data Mining _2bisacsh
650		7	_aMATHEMATICS / Arithmetic _2bisacsh
650		0	_aAutomatic hypothesis formation.
650		0	_aData mining.
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781003091448
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999			_c5774 _d5774