LEADER 04078nam 22007215 450 001 9910484664603321 005 20251226202946.0 010 $a3-540-75549-7 024 7 $a10.1007/978-3-540-75549-4 035 $a(CKB)1000000000490733 035 $a(SSID)ssj0000318577 035 $a(PQKBManifestationID)11224870 035 $a(PQKBTitleCode)TC0000318577 035 $a(PQKBWorkID)10311442 035 $a(PQKB)10918869 035 $a(DE-He213)978-3-540-75549-4 035 $a(MiAaPQ)EBC3068604 035 $a(PPN)123728614 035 $a(MiAaPQ)EBC336999 035 $a(BIP)34165065 035 $a(BIP)17712308 035 $a(EXLCZ)991000000000490733 100 $a20100301d2007 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aKnowledge Discovery in Inductive Databases $e5th International Workshop, KDID 2006 Berlin, Germany, September 18th, 2006 Revised Selected and Invited Papers /$fedited by Saso Dzeroski, Jan Struyf 205 $a1st ed. 2007. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2007. 215 $a1 online resource (X, 301 p.) 225 1 $aInformation Systems and Applications, incl. Internet/Web, and HCI,$x2946-1642 ;$v4747 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a3-540-75548-9 320 $aIncludes bibliographical references and index. 327 $aInvited Talk -- Value, Cost, and Sharing: Open Issues in Constrained Clustering -- Contributed Papers -- Mining Bi-sets in Numerical Data -- Extending the Soft Constraint Based Mining Paradigm -- On Interactive Pattern Mining from Relational Databases -- Analysis of Time Series Data with Predictive Clustering Trees -- Integrating Decision Tree Learning into Inductive Databases -- Using a Reinforced Concept Lattice to Incrementally Mine Association Rules from Closed Itemsets -- An Integrated Multi-task Inductive Database VINLEN: Initial Implementation and Early Results -- Beam Search Induction and Similarity Constraints for Predictive Clustering Trees -- Frequent Pattern Mining and Knowledge Indexing Based on Zero-Suppressed BDDs -- Extracting Trees of Quantitative Serial Episodes -- IQL: A Proposal for an Inductive Query Language -- Mining Correct Properties in Incomplete Databases -- Efficient Mining Under Rich Constraints Derived from Various Datasets -- Three Strategies for Concurrent Processing of Frequent Itemset Queries Using FP-Growth -- Discussion Paper -- Towards a General Framework for Data Mining. 330 $aThis book constitutes the thoroughly refereed joint postproceedings of the 5th International Workshop on Knowledge Discovery in Inductive Databases, KDID 2006, held in association with ECML/PKDD. Bringing together the fields of databases, machine learning, and data mining, the papers address various current topics in knowledge discovery and data mining in the framework of inductive databases such as constraint-based mining, database technology and inductive querying. 410 0$aInformation Systems and Applications, incl. Internet/Web, and HCI,$x2946-1642 ;$v4747 606 $aData structures (Computer science) 606 $aInformation theory 606 $aDatabase management 606 $aArtificial intelligence 606 $aData Structures and Information Theory 606 $aDatabase Management 606 $aArtificial Intelligence 615 0$aData structures (Computer science). 615 0$aInformation theory. 615 0$aDatabase management. 615 0$aArtificial intelligence. 615 14$aData Structures and Information Theory. 615 24$aDatabase Management. 615 24$aArtificial Intelligence. 676 $a005.74 701 $aDzeroski$b Saso$f1968-$0856308 701 $aStruyf$b Jan$01763996 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484664603321 996 $aKnowledge discovery in inductive databases$94204731 997 $aUNINA