LEADER 04925nam 22006255 450 001 996466100803316 005 20200705201427.0 010 $a3-540-33293-6 024 7 $a10.1007/11733492 035 $a(CKB)1000000000232909 035 $a(SSID)ssj0000318579 035 $a(PQKBManifestationID)11212543 035 $a(PQKBTitleCode)TC0000318579 035 $a(PQKBWorkID)10311616 035 $a(PQKB)10314384 035 $a(DE-He213)978-3-540-33293-0 035 $a(MiAaPQ)EBC3067996 035 $a(PPN)123133238 035 $a(EXLCZ)991000000000232909 100 $a20100301d2006 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aKnowledge Discovery in Inductive Databases$b[electronic resource] $e4th International Workshop, KDID 2005, Porto, Portugal, October 3, 2005, Revised Selected and Invited Papers /$fedited by Francesco Bonchi, Jean-Francois Boulicaut 205 $a1st ed. 2006. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2006. 215 $a1 online resource (VIII, 252 p.) 225 1 $aInformation Systems and Applications, incl. Internet/Web, and HCI ;$v3933 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-33292-8 320 $aIncludes bibliographical references and index. 327 $aInvited Papers -- Data Mining in Inductive Databases -- Mining Databases and Data Streams with Query Languages and Rules -- Contributed Papers -- Memory-Aware Frequent k-Itemset Mining -- Constraint-Based Mining of Fault-Tolerant Patterns from Boolean Data -- Experiment Databases: A Novel Methodology for Experimental Research -- Quick Inclusion-Exclusion -- Towards Mining Frequent Queries in Star Schemes -- Inductive Databases in the Relational Model: The Data as the Bridge -- Transaction Databases, Frequent Itemsets, and Their Condensed Representations -- Multi-class Correlated Pattern Mining -- Shaping SQL-Based Frequent Pattern Mining Algorithms -- Exploiting Virtual Patterns for Automatically Pruning the Search Space -- Constraint Based Induction of Multi-objective Regression Trees -- Learning Predictive Clustering Rules. 330 $aThe4thInternationalWorkshoponKnowledgeDiscoveryinInductiveDatabases (KDID 2005) was held in Porto, Portugal, on October 3, 2005 in conjunction with the 16th European Conference on Machine Learning and the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases. Ever since the start of the ?eld of data mining, it has been realized that the integration of the database technology into knowledge discovery processes was a crucial issue. This vision has been formalized into the inductive database perspective introduced by T. Imielinski and H. Mannila (CACM 1996, 39(11)). The main idea is to consider knowledge discovery as an extended querying p- cess for which relevant query languages are to be speci?ed. Therefore, inductive databases might contain not only the usual data but also inductive gener- izations (e. g. , patterns, models) holding within the data. Despite many recent developments, there is still a pressing need to understand the central issues in inductive databases. Constraint-based mining has been identi?ed as a core technology for inductive querying, and promising results have been obtained for rather simple types of patterns (e. g. , itemsets, sequential patterns). However, constraint-based mining of models remains a quite open issue. Also, coupling schemes between the available database technology and inductive querying p- posals are not yet well understood. Finally, the de?nition of a general purpose inductive query language is still an on-going quest. 410 0$aInformation Systems and Applications, incl. Internet/Web, and HCI ;$v3933 606 $aData structures (Computer science) 606 $aDatabase management 606 $aArtificial intelligence 606 $aData Structures and Information Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/I15009 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aData structures (Computer science). 615 0$aDatabase management. 615 0$aArtificial intelligence. 615 14$aData Structures and Information Theory. 615 24$aDatabase Management. 615 24$aArtificial Intelligence. 676 $a005.74 702 $aBonchi$b Francesco$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBoulicaut$b Jean-Francois$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a996466100803316 996 $aKnowledge Discovery in Inductive Databases$9771958 997 $aUNISA