LEADER 06506nam 22008175 450 001 996199681803316 005 20200702091911.0 010 $a3-319-11812-9 024 7 $a10.1007/978-3-319-11812-3 035 $a(CKB)3710000000249783 035 $a(SSID)ssj0001354104 035 $a(PQKBManifestationID)11758987 035 $a(PQKBTitleCode)TC0001354104 035 $a(PQKBWorkID)11322206 035 $a(PQKB)10451162 035 $a(DE-He213)978-3-319-11812-3 035 $a(MiAaPQ)EBC6296470 035 $a(MiAaPQ)EBC5594354 035 $a(Au-PeEL)EBL5594354 035 $a(OCoLC)892732154 035 $a(PPN)181352214 035 $a(EXLCZ)993710000000249783 100 $a20140927d2014 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aDiscovery Science$b[electronic resource] $e17th International Conference, DS 2014, Bled, Slovenia, October 8-10, 2014, Proceedings /$fedited by Sa?o D?eroski, Pan?e Panov, Dragi Kocev, Ljup?o Todorovski 205 $a1st ed. 2014. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2014. 215 $a1 online resource (XXII, 364 p. 111 illus.) 225 1 $aLecture Notes in Artificial Intelligence ;$v8777 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-319-11811-0 320 $aIncludes bibliographical references and index. 327 $aExplaining Mixture Models through Semantic Pattern Mining and Banded Matrix Visualization -- Big Data Analysis of StockTwits to Predict Sentiments in the Stock Market -- Synthetic Sequence Generator for Recommender Systems ? Memory Biased Random Walk on a Sequence Multilayer Network -- Predicting Sepsis Severity from Limited Temporal Observations -- Completion Time and Next Activity Prediction of Processes Using Sequential Pattern Mining -- Antipattern Discovery in Ethiopian Bagana Songs -- Categorize, Cluster, and Classify: A 3-C Strategy for Scientific Discovery in the Medical Informatics Platform of the Human Brain Project -- Multilayer Clustering: A Discovery Experiment on Country Level Trading Data -- Medical Document Mining Combining Image Exploration and Text Characterization -- Mining Cohesive Itemsets in Graphs -- Mining Rank Data -- Link Prediction on the Semantic MEDLINE Network: An Approach to Literature-Based Discovery -- Medical Image Retrieval Using Multimodal Data -- Fast Computation of the Tree Edit Distance between Unordered Trees Using IP Solvers -- Probabilistic Active Learning: Towards Combining Versatility, Optimality and Efficiency -- Incremental Learning with Social Media Data to Predict Near Real-Time Events -- Stacking Label Features for Learning Multilabel Rules -- Selective Forgetting for Incremental Matrix Factorization in Recommender Systems -- Providing Concise Database Covers Instantly by Recursive Tile Sampling -- Resampling-Based Framework for Estimating Node Centrality of Large Social Network -- Detecting Maximum k-Plex with Iterative Proper l-Plex Search -- Exploiting Bhattacharyya Similarity Measure to Diminish User Cold-Start Problem in Sparse Data -- Failure Prediction ? An Application in the Railway Industry -- Wind Power Forecasting Using Time Series Cluster Analysis -- Feature Selection in Hierarchical Feature Spaces -- Incorporating Regime Metrics into Latent Variable Dynamic Models to Detect Early-Warning Signals of Functional Changes in Fisheries Ecology -- An Efficient Algorithm for Enumerating Chordless Cycles and Chordless Paths -- Algorithm Selection on Data Streams -- Sparse Coding for Key Node Selection over Networks -- Variational Dependent Multi-output Gaussian Process Dynamical Systems. 330 $aThis book constitutes the proceedings of the 17th International Conference on Discovery Science, DS 2014, held in Bled, Slovenia, in October 2014. The 30 full papers included in this volume were carefully reviewed and selected from 62 submissions. The papers cover topics such as: computational scientific discovery; data mining and knowledge discovery; machine learning and statistical methods; computational creativity; mining scientific data; data and knowledge visualization; knowledge discovery from scientific literature; mining text, unstructured and multimedia data; mining structured and relational data; mining temporal and spatial data; mining data streams; network analysis; discovery informatics; discovery and experimental workflows; knowledge capture and scientific ontologies; data and knowledge integration; logic and philosophy of scientific discovery; and applications of computational methods in various scientific domains. 410 0$aLecture Notes in Artificial Intelligence ;$v8777 606 $aArtificial intelligence 606 $aData mining 606 $aInformation storage and retrieval 606 $aDatabase management 606 $aAlgorithms 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aInformation Storage and Retrieval$3https://scigraph.springernature.com/ontologies/product-market-codes/I18032 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 615 0$aArtificial intelligence. 615 0$aData mining. 615 0$aInformation storage and retrieval. 615 0$aDatabase management. 615 0$aAlgorithms. 615 14$aArtificial Intelligence. 615 24$aData Mining and Knowledge Discovery. 615 24$aInformation Storage and Retrieval. 615 24$aDatabase Management. 615 24$aAlgorithm Analysis and Problem Complexity. 676 $a006.3 702 $aD?eroski$b Sa?o$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aPanov$b Pan?e$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKocev$b Dragi$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aTodorovski$b Ljup?o$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996199681803316 996 $aDiscovery Science$9772321 997 $aUNISA