LEADER 04276nam 22006135 450 001 9910483610503321 005 20251226203650.0 024 7 $a10.1007/b106731 035 $a(CKB)1000000000212866 035 $a(SSID)ssj0000318578 035 $a(PQKBManifestationID)11239154 035 $a(PQKBTitleCode)TC0000318578 035 $a(PQKBWorkID)10311158 035 $a(PQKB)11482275 035 $a(DE-He213)978-3-540-31841-5 035 $a(MiAaPQ)EBC3067817 035 $a(PPN)123092663 035 $a(BIP)11548152 035 $a(EXLCZ)991000000000212866 100 $a20100715d2005 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aKnowledge Discovery in Inductive Databases $eThird International Workshop, KDID 2004, Pisa, Italy, September 20, 2004, Revised Selected and Invited Papers /$fedited by Arno Siebes 205 $a1st ed. 2005. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2005. 215 $a1 online resource (VIII, 200 p.) 225 1 $aInformation Systems and Applications, incl. Internet/Web, and HCI,$x2946-1642 ;$v3377 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$aPrinted edition: 9783540250821 320 $aIncludes bibliographical references and index. 327 $aInvited Paper -- Models and Indices for Integrating Unstructured Data with a Relational Database -- Contributed Papers -- Constraint Relaxations for Discovering Unknown Sequential Patterns -- Mining Formal Concepts with a Bounded Number of Exceptions from Transactional Data -- Theoretical Bounds on the Size of Condensed Representations -- Mining Interesting XML-Enabled Association Rules with Templates -- Database Transposition for Constrained (Closed) Pattern Mining -- An Efficient Algorithm for Mining String Databases Under Constraints -- An Automata Approach to Pattern Collections -- Implicit Enumeration of Patterns -- Condensed Representation of EPs and Patterns Quantified by Frequency-Based Measures. 330 $aThe3rdInternationalWorkshoponKnowledgeDiscoveryinInductiveDatabases (KDID 2004) was held in Pisa, Italy, on September 20, 2004 as part of the 15th European Conference on Machine Learning and the 8th European Conference onPrinciplesandPracticeofKnowledgeDiscoveryinDatabases(ECML/PKDD 2004). Ever since the start of the ?eld of data mining, it has been realized that the knowledge discovery and data mining process should be integrated into database technology. This idea has been formalized in the concept of inductive databases, introduced by Imielinski and Mannila (CACM 1996, 39(11)). In general, an inductive database is a database that supports data mining and the knowledge discovery process in a natural and elegant way. In addition to the usual data, it also contains inductive generalizations (e.g., patterns, models) extracted from the data. Within this framework, knowledge discovery is an - teractive process in which users can query the inductive database to gain insight to the data and the patterns and models within that data. Despite many recent developments, there still exists a pressing need to - derstandthecentralissuesininductivedatabases.Thisworkshopaimedtobring together database and data mining researchers and practitioners who are int- ested in the numerous challenges that inductive databases o'ers. This workshop followed the previous two workshops: KDID 2002 held in Helsinki, Finland, and KDID 2003 held in Cavtat-Dubrovnik, Croatia. 410 0$aInformation Systems and Applications, incl. Internet/Web, and HCI,$x2946-1642 ;$v3377 606 $aDatabase management 606 $aArtificial intelligence 606 $aDatabase Management 606 $aArtificial Intelligence 615 0$aDatabase management. 615 0$aArtificial intelligence. 615 14$aDatabase Management. 615 24$aArtificial Intelligence. 676 $a005.74 701 $aGoethals$b Bart$0973958 701 $aSiebes$b Arno$f1958-$01757044 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483610503321 996 $aKnowledge discovery in inductive databases$94195681 997 $aUNINA