LEADER 04868nam 22007935 450 001 996466164103316 005 20200706030005.0 010 $a3-540-32548-4 024 7 $a10.1007/11677437 035 $a(CKB)1000000000232832 035 $a(SSID)ssj0000317106 035 $a(PQKBManifestationID)11244120 035 $a(PQKBTitleCode)TC0000317106 035 $a(PQKBWorkID)10286446 035 $a(PQKB)10586064 035 $a(DE-He213)978-3-540-32548-2 035 $a(MiAaPQ)EBC3067892 035 $a(MiAaPQ)EBC3180728 035 $a(MiAaPQ)EBC571806 035 $a(Au-PeEL)EBL571806 035 $a(OCoLC)663096739 035 $a(PPN)123131723 035 $a(EXLCZ)991000000000232832 100 $a20100320d2006 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aData Mining$b[electronic resource] $eTheory, Methodology, Techniques, and Applications /$fedited by Graham J. Williams, Simeon J. Simoff 205 $a1st ed. 2006. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2006. 215 $a1 online resource (XI, 331 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v3755 300 $aChapters based on the works presented at the Australasian data mining conference series and industry forums. 311 $a3-540-32547-6 320 $aIncludes bibliographical references and index. 327 $a1: State-of-the-Art in Research -- Generality Is Predictive of Prediction Accuracy -- Visualisation and Exploration of Scientific Data Using Graphs -- A Case-Based Data Mining Platform -- Consolidated Trees: An Analysis of Structural Convergence -- K Nearest Neighbor Edition to Guide Classification Tree Learning: Motivation and Experimental Results -- Efficiently Identifying Exploratory Rules? Significance -- Mining Value-Based Item Packages ? An Integer Programming Approach -- Decision Theoretic Fusion Framework for Actionability Using Data Mining on an Embedded System -- Use of Data Mining in System Development Life Cycle -- Mining MOUCLAS Patterns and Jumping MOUCLAS Patterns to Construct Classifiers -- A Probabilistic Geocoding System Utilising a Parcel Based Address File -- Decision Models for Record Linkage -- Intelligent Document Filter for the Internet -- Informing the Curious Negotiator: Automatic News Extraction from the Internet -- Text Mining for Insurance Claim Cost Prediction -- An Application of Time-Changing Feature Selection -- A Data Mining Approach to Analyze the Effect of Cognitive Style and Subjective Emotion on the Accuracy of Time-Series Forecasting -- A Multi-level Framework for the Analysis of Sequential Data -- 2: State-of-the-Art in Applications -- Hierarchical Hidden Markov Models: An Application to Health Insurance Data -- Identifying Risk Groups Associated with Colorectal Cancer -- Mining Quantitative Association Rules in Protein Sequences -- Mining X-Ray Images of SARS Patients -- The Scamseek Project ? Text Mining for Financial Scams on the Internet -- A Data Mining Approach for Branch and ATM Site Evaluation -- The Effectiveness of Positive Data Sharing in Controlling the Growth of Indebtedness in Hong Kong Credit Card Industry. 410 0$aLecture Notes in Artificial Intelligence ;$v3755 606 $aDatabase management 606 $aArtificial intelligence 606 $aComputers 606 $aInformation storage and retrieval 606 $aPattern recognition 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aComputation by Abstract Devices$3https://scigraph.springernature.com/ontologies/product-market-codes/I16013 606 $aInformation Storage and Retrieval$3https://scigraph.springernature.com/ontologies/product-market-codes/I18032 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 615 0$aDatabase management. 615 0$aArtificial intelligence. 615 0$aComputers. 615 0$aInformation storage and retrieval. 615 0$aPattern recognition. 615 14$aDatabase Management. 615 24$aArtificial Intelligence. 615 24$aComputation by Abstract Devices. 615 24$aInformation Storage and Retrieval. 615 24$aPattern Recognition. 676 $a005.74 702 $aWilliams$b Graham J$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSimoff$b Simeon J$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996466164103316 996 $aData Mining$9772581 997 $aUNISA