LEADER 00997nam--2200361---450- 001 990001933050203316 005 20050217114521.0 035 $a000193305 035 $aUSA01000193305 035 $a(ALEPH)000193305USA01 035 $a000193305 100 $a20040816d1981----km-y0itay0103----ba 101 0 $aita 102 $aIT 105 $a||||||||001yy 200 1 $aCristianesimo e Impero romano$fGiovanni Squitieri 210 $aSalerno$cDottrinari$d1981 215 $a432 p.$d24 cm 410 0$12001 454 1$12001 461 1$1001-------$12001 606 0 $aCristianesimo e impero romano 676 $a270.2 700 1$aSQUITIERI,$bGiovanni$0565396 801 0$aIT$bsalbc$gISBD 912 $a990001933050203316 951 $aII.2. 432(Varie 149)$b18883 L.M.$cVarie 149 959 $aBK 969 $aUMA 979 $aSIAV6$b10$c20040816$lUSA01$h1333 979 $aCOPAT3$b90$c20050217$lUSA01$h1145 996 $aCristianesimo e Impero romano$91044876 997 $aUNISA LEADER 04501nam 2200577Ia 450 001 9910739418703321 005 20200520144314.0 010 $a9783642356506 010 $a3642356508 024 7 $a10.1007/978-3-642-35650-6 035 $a(CKB)3400000000102938 035 $a(SSID)ssj0000878384 035 $a(PQKBManifestationID)11476008 035 $a(PQKBTitleCode)TC0000878384 035 $a(PQKBWorkID)10836365 035 $a(PQKB)10494738 035 $a(DE-He213)978-3-642-35650-6 035 $a(MiAaPQ)EBC3071039 035 $a(PPN)168329077 035 $a(EXLCZ)993400000000102938 100 $a20130107d2013 uy 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aAction rules mining /$fAgnieszka Dardzinska 205 $a1st ed. 2013. 210 $aBerlin ;$aNew York $cSpringer$dc2013 215 $a1 online resource (X, 98 p.) 225 1 $aStudies in computational intelligence,$x1860-949X ;$v468 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a9783642356490 311 08$a3642356494 320 $aIncludes bibliographical references. 327 $t1.$tIntroduction --$g2.$tInformation Systems --$g2.1.$tTypes of Information Systems --$g2.2.$tTypes of Incomplete Information Systems --$g2.3.$tSimple Query Language --$g2.3.1.$tStandard Interpretation of Queries in Complete Information Systems --$g2.3.2.$tStandard Interpretation of Queries in Incomplete Information Systems --$g2.4.$tRules --$g2.5.$tDistributed Information Systems --$g2.6.$tDecision Systems --$g2.7.$tPartially Incomplete Information Systems --$g2.8.$tExtracting Classification Rules --$g2.8.1.$tAttribute Dependency and Coverings --$g2.8.2.$tSystem LERS --$g2.8.3.$tAlgorithm for Finding the Set of All Coverings (LEM1) --$g2.8.4.$tAlgorithm LEM2 --$g2.8.5.$tAlgorithm for Extracting Rules from Incomplete --$gDecision System (ERID) --$g2.9.$tChase Algorithms --$g2.9.1.$tTableaux Systems and Chase --$g2.9.2.$tHandling Incomplete Values Using CHASE1 Algorithm --$g2.9.3.$tHandling Incomplete Values Using CHASE2 Algorithm --$gX Contents --$g3.$tActionRules --$g3.1.$tMain Assumptions --$g3.2.$tAction Rules from Classification Rules --$g3.2.1.$tSystem DEAR --$g3.2.2.$tSystem DEAR2 --$g3.3.$tE-action Rules --$g3.3.1.$tARAS Algorithm. --$g3.4.$tAction Rules Tightly Coupled Framework --$g3.5.$tCost and Feasibility. --$g3.6.$tAssociation Action Rules --$g3.6.1.$tFrequent Action Sets --$g3.7.$tRepresentative Association Action Rules --$g3.8.$tSimple Association Action Rules --$g3.9.$tAction Reducts --$g3.9.1.$tExperiments and Testing --$g3.10.$tMeta-action --$g3.10.1.$tDiscovering Action Paths. 330 $aWe are surrounded by data, numerical, categorical and otherwise, which must to be analyzed and processed to convert it into information that instructs, answers or aids understanding and decision making. Data analysts in many disciplines such as business, education or medicine, are frequently asked to analyze new data sets which are often composed of numerous tables possessing different properties. They try to find completely new correlations between attributes and show new possibilities for users.   Action rules mining discusses some of data mining and knowledge discovery principles and then describe representative concepts, methods and algorithms connected with action. The author introduces the formal definition of action rule, notion of a simple association action rule and a representative action rule, the cost of association action rule, and gives a strategy how to construct simple association action rules of a lowest cost. A new approach for generating action rules from datasets with numerical attributes by incorporating a tree classifier and a pruning step based on meta-actions is also presented. In this book we can find fundamental concepts necessary for designing, using and implementing action rules as well. Detailed algorithms are provided with necessary explanation and illustrative examples. 410 0$aStudies in computational intelligence ;$vv. 468. 606 $aData mining 606 $aAssociation rule mining 615 0$aData mining. 615 0$aAssociation rule mining. 676 $a006.3/12 700 $aDardzinska$b Agnieszka$01424056 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910739418703321 996 $aAction Rules Mining$93552911 997 $aUNINA