LEADER 04377nam 22007935 450 001 9910484838003321 005 20251226203155.0 010 $a3-540-31351-6 024 7 $a10.1007/11615576 035 $a(CKB)1000000000232771 035 $a(SSID)ssj0000316987 035 $a(PQKBManifestationID)11258588 035 $a(PQKBTitleCode)TC0000316987 035 $a(PQKBWorkID)10287065 035 $a(PQKB)11372535 035 $a(DE-He213)978-3-540-31351-9 035 $a(MiAaPQ)EBC3068391 035 $a(PPN)123130662 035 $a(BIP)32372860 035 $a(BIP)13290800 035 $a(EXLCZ)991000000000232771 100 $a20100417d2006 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aConstraint-Based Mining and Inductive Databases $eEuropean Workshop on Inductive Databases and Constraint Based Mining, Hinterzarten, Germany, March 11-13, 2004, Revised Selected Papers /$fedited by Jean-Francois Boulicaut, Luc De Raedt, Heikki Mannila 205 $a1st ed. 2006. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2006. 215 $a1 online resource (X, 404 p.) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v3848 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a3-540-31331-1 320 $aIncludes bibliographical references and index. 327 $aThe Hows, Whys, and Whens of Constraints in Itemset and Rule Discovery -- A Relational Query Primitive for Constraint-Based Pattern Mining -- To See the Wood for the Trees: Mining Frequent Tree Patterns -- A Survey on Condensed Representations for Frequent Sets -- Adaptive Strategies for Mining the Positive Border of Interesting Patterns: Application to Inclusion Dependencies in Databases -- Computation of Mining Queries: An Algebraic Approach -- Inductive Queries on Polynomial Equations -- Mining Constrained Graphs: The Case of Workflow Systems -- CrossMine: Efficient Classification Across Multiple Database Relations -- Remarks on the Industrial Application of Inductive Database Technologies -- How to Quickly Find a Witness -- Relevancy in Constraint-Based Subgroup Discovery -- A Novel Incremental Approach to Association Rules Mining in Inductive Databases -- Employing Inductive Databases in Concrete Applications -- Contribution to Gene Expression Data Analysis by Means of Set Pattern Mining -- Boolean Formulas and Frequent Sets -- Generic Pattern Mining Via Data Mining Template Library -- Inductive Querying for Discovering Subgroups and Clusters. 330 $aThe interconnected ideas of inductive databases and constraint-based mining are appealing and have the potential to radically change the theory and practice of data mining and knowledge discovery. This book reports on the results of the European IST project "cInQ" (consortium on knowledge discovery by Inductive Queries) and its final workshop entitled Constraint-Based Mining and Inductive Databases organized in Hinterzarten, Germany in March 2004. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v3848 606 $aArtificial intelligence 606 $aComputer science 606 $aDatabase management 606 $aInformation storage and retrieval systems 606 $aPattern recognition systems 606 $aArtificial Intelligence 606 $aTheory of Computation 606 $aDatabase Management 606 $aInformation Storage and Retrieval 606 $aAutomated Pattern Recognition 615 0$aArtificial intelligence. 615 0$aComputer science. 615 0$aDatabase management. 615 0$aInformation storage and retrieval systems. 615 0$aPattern recognition systems. 615 14$aArtificial Intelligence. 615 24$aTheory of Computation. 615 24$aDatabase Management. 615 24$aInformation Storage and Retrieval. 615 24$aAutomated Pattern Recognition. 676 $a005.74 701 $aBoulicaut$b Jean-Franc?ois$00 701 $aRaedt$b Luc de$f1964-$01754852 701 $aMannila$b Heikki$0145716 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484838003321 996 $aConstraint-based mining and inductive databases$94198209 997 $aUNINA