000 02860nam a22004575i 4500
001 978-3-642-35650-6
003 DE-He213
005 20200421111848.0
007 cr nn 008mamaa
008 121214s2013 gw | s |||| 0|eng d
020 _a9783642356506
_9978-3-642-35650-6
024 7 _a10.1007/978-3-642-35650-6
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aDardzinska, Agnieszka.
_eauthor.
245 1 0 _aAction Rules Mining
_h[electronic resource] /
_cby Agnieszka Dardzinska.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2013.
300 _aX, 98 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 ;
_v468
520 _aWe are surrounded by data, numerical, categorical and otherwise, which must to be analyzed and processed to convert it into information that instructs, answers or aids understanding and decision making. Data analysts in many disciplines such as business, education or medicine, are frequently asked to analyze new data sets which are often composed of numerous tables possessing different properties. They try to find completely new correlations between attributes and show new possibilities for users.   Action rules mining discusses some of data mining and knowledge discovery principles and then describe representative concepts, methods and algorithms connected with action. The author introduces the formal definition of action rule, notion of a simple association action rule and a representative action rule, the cost of association action rule, and gives a strategy how to construct simple association action rules of a lowest cost. A new approach for generating action rules from datasets with numerical attributes by incorporating a tree classifier and a pruning step based on meta-actions is also presented. In this book we can find fundamental concepts necessary for designing, using and implementing action rules as well. Detailed algorithms are provided with necessary explanation and illustrative examples.
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:
_z9783642356490
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v468
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-35650-6
912 _aZDB-2-ENG
942 _cEBK
999 _c55946
_d55946