LEADER 04044nam 22006855 450 001 9910299679503321 005 20200702062243.0 010 $a3-319-13212-1 024 7 $a10.1007/978-3-319-13212-9 035 $a(CKB)3710000000324648 035 $a(EBL)1968446 035 $a(OCoLC)908089639 035 $a(SSID)ssj0001408088 035 $a(PQKBManifestationID)11766013 035 $a(PQKBTitleCode)TC0001408088 035 $a(PQKBWorkID)11346458 035 $a(PQKB)11559024 035 $a(DE-He213)978-3-319-13212-9 035 $a(MiAaPQ)EBC1968446 035 $a(PPN)183154010 035 $a(EXLCZ)993710000000324648 100 $a20141227d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aAdvances in Knowledge Discovery in Databases /$fby Animesh Adhikari, Jhimli Adhikari 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (377 p.) 225 1 $aIntelligent Systems Reference Library,$x1868-4394 ;$v79 300 $aDescription based upon print version of record. 311 $a3-319-13211-3 320 $aIncludes bibliographical references and index. 327 $aIntroduction -- Synthesizing conditional patterns in a database -- Synthesizing arbitrary Boolean expressions induced by frequent itemsets -- Measuring association among items in a database -- Mining association rules induced by item and quantity purchased -- Mining patterns different related databases -- Mining icebergs in different time-stamped data sources.-Synthesizing exceptional patterns in different data Sources -- Clustering items in time-stamped databases -- Synthesizing some extreme association rules from multiple databases -- Clustering local frequency items in multiple data sources -- Mining patterns of select items in different data sources -- Mining calendar-based periodic patterns in time-stamped data -- Measuring influence of an item in time-stamped databases -- Clustering multiple databases induced by local patterns -- Enhancing quality of patterns in multiple related databases -- Concluding remarks. 330 $aThis book presents recent advances in Knowledge discovery in databases (KDD) with a focus on the areas of market basket database, time-stamped databases and multiple related databases. Various interesting and intelligent algorithms are reported on data mining tasks. A large number of association measures are presented, which play significant roles in decision support applications. This book presents, discusses and contrasts new developments in mining time-stamped data, time-based data analyses, the identification of temporal patterns, the mining of multiple related databases, as well as local patterns analysis.  . 410 0$aIntelligent Systems Reference Library,$x1868-4394 ;$v79 606 $aComputational intelligence 606 $aData mining 606 $aArtificial intelligence 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aComputational intelligence. 615 0$aData mining. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aData Mining and Knowledge Discovery. 615 24$aArtificial Intelligence. 676 $a006.312 700 $aAdhikari$b Animesh$4aut$4http://id.loc.gov/vocabulary/relators/aut$0720955 702 $aAdhikari$b Jhimli$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299679503321 996 $aAdvances in Knowledge Discovery in Databases$92512384 997 $aUNINA