04229nam 22007215 450 99646609790331620200701063816.03-540-31351-610.1007/11615576(CKB)1000000000232771(SSID)ssj0000316987(PQKBManifestationID)11258588(PQKBTitleCode)TC0000316987(PQKBWorkID)10287065(PQKB)11372535(DE-He213)978-3-540-31351-9(MiAaPQ)EBC3068391(PPN)123130662(EXLCZ)99100000000023277120100417d2006 u| 0engurnn|008mamaatxtccrConstraint-Based Mining and Inductive Databases[electronic resource] European Workshop on Inductive Databases and Constraint Based Mining, Hinterzarten, Germany, March 11-13, 2004, Revised Selected Papers /edited by Jean-Francois Boulicaut, Luc De Raedt, Heikki Mannila1st ed. 2006.Berlin, Heidelberg :Springer Berlin Heidelberg :Imprint: Springer,2006.1 online resource (X, 404 p.) Lecture Notes in Artificial Intelligence ;3848Bibliographic Level Mode of Issuance: Monograph3-540-31331-1 Includes bibliographical references and index.The 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.Lecture Notes in Artificial Intelligence ;3848Artificial intelligenceComputersDatabase managementInformation storage and retrievalPattern recognitionArtificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Computation by Abstract Deviceshttps://scigraph.springernature.com/ontologies/product-market-codes/I16013Database Managementhttps://scigraph.springernature.com/ontologies/product-market-codes/I18024Information Storage and Retrievalhttps://scigraph.springernature.com/ontologies/product-market-codes/I18032Pattern Recognitionhttps://scigraph.springernature.com/ontologies/product-market-codes/I2203XArtificial intelligence.Computers.Database management.Information storage and retrieval.Pattern recognition.Artificial Intelligence.Computation by Abstract Devices.Database Management.Information Storage and Retrieval.Pattern Recognition.005.74Boulicaut Jean-Francoisedthttp://id.loc.gov/vocabulary/relators/edtDe Raedt Lucedthttp://id.loc.gov/vocabulary/relators/edtMannila Heikkiedthttp://id.loc.gov/vocabulary/relators/edtBOOK996466097903316Constraint-Based Mining and Inductive Databases772146UNISA