LEADER 01545oas 2200541 a 450 001 9910145800703321 005 20251105213014.0 035 $a(OCoLC)56391068 035 $a(CONSER) 2004209889 035 $a(CKB)1000000000018629 035 $a(DE-599)ZDB2036327-8 035 $a(EXLCZ)991000000000018629 100 $a20040830a20019999 sy 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aElectronic journal of structural engineering $eEJSE 210 $a[Melbourne] $cEJSE International$dİ2001- 300 $aRefereed/Peer-reviewed 300 $aTitle from journal home page (publisher's Web site, viewed Aug. 30, 2004). 311 08$a1443-9255 517 3 $aEJSE 517 1 $aEJSE, International journal 606 $aStructural engineering$vPeriodicals 606 $aStructural engineering$2fast$3(OCoLC)fst01135658 608 $aPeriodicals.$2fast 615 0$aStructural engineering 615 7$aStructural engineering. 712 02$aUniversity of Melbourne.$bDepartment of Civil and Environmental Engineering. 801 0$bDLC 801 1$bDLC 801 2$bCUS 801 2$bTJC 801 2$bOCLCQ 801 2$bOCLCO 801 2$bOCLCA 801 2$bOCLCF 801 2$bOCLCO 801 2$bOCLCQ 801 2$bSFB 801 2$bDLC 801 2$bOCLCQ 906 $aJOURNAL 912 $a9910145800703321 996 $aElectronic journal of structural engineering$92225169 997 $aUNINA LEADER 04647nam 22007575 450 001 9910483942503321 005 20251226195358.0 010 $a3-540-68416-6 024 7 $a10.1007/978-3-540-68416-9 035 $a(CKB)1000000000440598 035 $a(SSID)ssj0000318950 035 $a(PQKBManifestationID)11243803 035 $a(PQKBTitleCode)TC0000318950 035 $a(PQKBWorkID)10337384 035 $a(PQKB)10549603 035 $a(DE-He213)978-3-540-68416-9 035 $a(MiAaPQ)EBC3068501 035 $a(PPN)127048138 035 $a(EXLCZ)991000000000440598 100 $a20100301d2008 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aMining Complex Data $eECML/PKDD 2007 Third International Workshop, MDC 2007, Warsaw, Poland, September 17-21, 2007, Revised Selected Papers /$fedited by Zbigniew W. Ras, Shusaku Tsumoto, Djamel A. Zighed 205 $a1st ed. 2008. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2008. 215 $a1 online resource (X, 265 p.) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v4944 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a3-540-68415-8 320 $aIncludes bibliographical references and index. 327 $aSession A1 -- Using Text Mining and Link Analysis for Software Mining -- Generalization-Based Similarity for Conceptual Clustering -- Trajectory Analysis of Laboratory Tests as Medical Complex Data Mining -- Session A2 -- Conceptual Clustering Applied to Ontologies -- Feature Selection: Near Set Approach -- Evaluating Accuracies of a Trading Rule Mining Method Based on Temporal Pattern Extraction -- Session A3 -- Discovering Word Meanings Based on Frequent Termsets -- Quality of Musical Instrument Sound Identification for Various Levels of Accompanying Sounds -- Discriminant Feature Analysis for Music Timbre Recognition and Automatic Indexing -- Session A4 -- Contextual Adaptive Clustering of Web and Text Documents with Personalization -- Improving Boosting by Exploiting Former Assumptions -- Discovery of Frequent Graph Patterns that Consist of the Vertices with the Complex Structures -- Session B1 -- Finding Composite Episodes -- Ordinal Classification with Decision Rules -- Data Mining of Multi-categorized Data -- ARAS: Action Rules Discovery Based on Agglomerative Strategy -- Session B2 -- Learning to Order: A Relational Approach -- Using Semantic Distance in a Content-Based Heterogeneous Information Retrieval System -- Using Secondary Knowledge to Support Decision Tree Classification of Retrospective Clinical Data -- POM Centric Multi-aspect Data Analysis for Investigating Human Problem Solving Function. 330 $aThis book constitutes the refereed proceedings of the Third International Workshop on Mining Complex Data, MCD 2007, held in Warsaw, Poland, in September 2007, co-located with ECML and PKDD 2007. The 20 revised full papers presented were carefully reviewed and selected; they present original results on knowledge discovery from complex data. In contrast to the typical tabular data, complex data can consist of heterogenous data types, can come from different sources, or live in high dimensional spaces. All these specificities call for new data mining strategies. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v4944 606 $aData mining 606 $aInformation storage and retrieval systems 606 $aArtificial intelligence$xData processing 606 $aInformation retrieval 606 $aComputer architecture 606 $aData Mining and Knowledge Discovery 606 $aInformation Storage and Retrieval 606 $aData Science 606 $aData Storage Representation 615 0$aData mining. 615 0$aInformation storage and retrieval systems. 615 0$aArtificial intelligence$xData processing. 615 0$aInformation retrieval. 615 0$aComputer architecture. 615 14$aData Mining and Knowledge Discovery. 615 24$aInformation Storage and Retrieval. 615 24$aData Science. 615 24$aData Storage Representation. 676 $a006.312 701 $aRas?$b Zbigniew$01756813 701 $aTsumoto$b Shusaku$f1963-$01756814 701 $aZighed$b Djamel A.$f1955-$01756815 712 12$aECML PKDD (Conference)$f(2007 :$eWarsaw, Poland) 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483942503321 996 $aMining complex data$94194337 997 $aUNINA