LEADER 05447nam 22007095 450 001 9910484961503321 005 20251226202142.0 010 $a3-642-37382-8 024 7 $a10.1007/978-3-642-37382-4 035 $a(CKB)3280000000007590 035 $a(DE-He213)978-3-642-37382-4 035 $a(SSID)ssj0000880044 035 $a(PQKBManifestationID)11546668 035 $a(PQKBTitleCode)TC0000880044 035 $a(PQKBWorkID)10872262 035 $a(PQKB)10595456 035 $a(MiAaPQ)EBC3093474 035 $a(PPN)169140288 035 $a(EXLCZ)993280000000007590 100 $a20130326d2013 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNew Frontiers in Mining Complex Patterns $eFirst International Workshop, NFMCP 2012, Held in Conjunction with ECML/PKDD 2012, Bristol, UK, September 24, 2012, Revised Selected Papers /$fedited by Annalisa Appice, Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew Ras 205 $a1st ed. 2013. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2013. 215 $a1 online resource (X, 231 p. 57 illus.) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v7765 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a3-642-37381-X 327 $aLearning with Configurable Operators and RL-Based Heuristics.- Reducing Examples in Relational Learning with Bounded-Treewidth Hypotheses.- Mining Complex Event Patterns in Computer Networks -- Learning in the Presence of Large Fluctuations: A Study of Aggregation and Correlation -- Machine Learning as an Objective Approach to Understanding Music.- Pair-Based Object-Driven Action Rules -- Effectively Grouping Trajectory Streams.- Healthcare Trajectory Mining by Combining Multidimensional Component and Itemsets -- Graph-Based Approaches to Clustering Network-Constrained Trajectory Data -- Finding the Most Descriptive Substructures in Graphs with Discrete and Numeric Labels.- Learning in Probabilistic Graphs Exploiting Language-Constrained Patterns.- Improving Robustness and Flexibility of Concept Taxonomy Learning from Text.- Discovering Evolution Chains in Dynamic Networks.- Supporting Information Spread in a Social  Internetworking Scenario.- Context-Aware Predictions on Business Processes: An Ensemble-Based Solution.  Reducing Examples in Relational Learning with Bounded-Treewidth Hypotheses.- Mining Complex Event Patterns in Computer Networks -- Learning in the Presence of Large Fluctuations: A Study of Aggregation and Correlation -- Machine Learning as an Objective Approach to Understanding Music.- Pair-Based Object-Driven Action Rules -- Effectively Grouping Trajectory Streams.- Healthcare Trajectory Mining by Combining Multidimensional Component and Itemsets -- Graph-Based Approaches to Clustering Network-Constrained Trajectory Data -- Finding the Most Descriptive Substructures in Graphs with Discrete and Numeric Labels.- Learning in Probabilistic Graphs Exploiting Language-ConstrainedPatterns.- Improving Robustness and Flexibility of Concept Taxonomy Learning from Text.- Discovering Evolution Chains in Dynamic Networks.- Supporting Information Spread in a Social  Internetworking Scenario.- Context-Aware Predictions on Business Processes: An Ensemble-Based Solution. . 330 $aThis book constitutes the thoroughly refereed conference proceedings of the First International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2012, held in conjunction with ECML/PKDD 2012, in Bristol, UK, in September 2012. The 15 revised full papers were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on mining rich (relational) datasets, mining complex patterns from miscellaneous data, mining complex patterns from trajectory and sequence data, and mining complex patterns from graphs and networks. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v7765 606 $aData mining 606 $aDatabase management 606 $aInformation storage and retrieval systems 606 $aArtificial intelligence 606 $aData Mining and Knowledge Discovery 606 $aDatabase Management 606 $aInformation Storage and Retrieval 606 $aArtificial Intelligence 615 0$aData mining. 615 0$aDatabase management. 615 0$aInformation storage and retrieval systems. 615 0$aArtificial intelligence. 615 14$aData Mining and Knowledge Discovery. 615 24$aDatabase Management. 615 24$aInformation Storage and Retrieval. 615 24$aArtificial Intelligence. 676 $a006.312 702 $aAppice$b Annalisa$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aCeci$b Michelangelo$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLoglisci$b Corrado$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aManco$b Giuseppe$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMasciari$b Elio$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRas$b Zbigniew$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910484961503321 996 $aNew Frontiers in Mining Complex Patterns$92177052 997 $aUNINA