000 03171nam a22004695i 4500
001 978-3-642-11737-4
003 DE-He213
005 20200421111845.0
007 cr nn 008mamaa
008 121227s2013 gw | s |||| 0|eng d
020 _a9783642117374
_9978-3-642-11737-4
024 7 _a10.1007/978-3-642-11737-4
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aRauch, Jan.
_eauthor.
245 1 0 _aObservational Calculi and Association Rules
_h[electronic resource] /
_cby Jan Rauch.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2013.
300 _aXXII, 298 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v469
505 0 _aPart I Logical Calculi of Association Rules -- Part II Classes of Association Rules -- Part III Results on Classes of Association Rules -- Part IV Applications and Research Challenges.
520 _aObservational calculi were introduced in the 1960's as a tool of logic of discovery. Formulas of observational calculi correspond to assertions on analysed data. Truthfulness of suitable assertions can lead to acceptance of new scientific hypotheses. The general goal was to automate the process of discovery of scientific knowledge using mathematical logic and statistics. The GUHA method for producing true formulas of observational calculi relevant to the given problem of scientific discovery was developed. Theoretically interesting and practically important results on observational calculi were achieved. Special attention was paid to formulas - couples of Boolean attributes derived from columns of the analysed data matrix. Association rules introduced in the 1990's can be seen as a special case of such formulas. New results on logical calculi and association rules were achieved. They can be seen as a logic of association rules. This can contribute to solving contemporary challenging problems of data mining research and practice. The book covers thoroughly the logic of association rules and puts it into the context of current research in data mining. Examples of applications of theoretical results to real problems are presented. New open problems and challenges are listed. Overall, the book is a valuable source of information for researchers as well as for teachers and students interested in data mining.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aComputational intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence (incl. Robotics).
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642117367
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v469
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-11737-4
912 _aZDB-2-ENG
942 _cEBK
999 _c55773
_d55773