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[null,"1.","Record Nr.",null,"UNISA996465760203316"]
[null,null,"Titolo",null,"Advances in Intelligent Data Analysis VIII [[electronic resource] ] : 8th International Symposium on Intelligent Data Analysis, IDA 2009, Lyon, France, August 31 - September 2, 2009, Proceedings / / edited by Niall M. Adams, Céline Robardet, Arno Siebes, Jean-Francois Boulicaut"]
[null,null,"Pubbl/distr/stampa",null,"Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2009"]
[null,null,"ISBN",null,"3-642-03915-4"]
[null,null,"Edizione",null,"[1st ed. 2009.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XIII, 418 p.)"]
[null,null,"Collana",null,"Information Systems and Applications, incl. Internet/Web, and HCI ; ; 5772"]
[null,null,"Classificazione",null,"DAT 703f"]
[{"$nulls":4},"MAT 620f"]
[{"$nulls":4},"SS 4800"]
[null,null,"Disciplina",null,"006.31222gerDNB"]
[null,null,"Soggetti",null,"Computers"]
[{"$nulls":4},"Information storage and retrieval"]
[{"$nulls":4},"Data mining"]
[{"$nulls":4},"Pattern recognition"]
[{"$nulls":4},"Data structures (Computer science)"]
[{"$nulls":4},"Information technology"]
[{"$nulls":4},"Business—Data processing"]
[{"$nulls":4},"Theory of Computation"]
[{"$nulls":4},"Information Storage and Retrieval"]
[{"$nulls":4},"Data Mining and Knowledge Discovery"]
[{"$nulls":4},"Pattern Recognition"]
[{"$nulls":4},"Data Structures"]
[{"$nulls":4},"IT in Business"]
[{"$nulls":4},"Kongress."]
[{"$nulls":4},"Lyon (2008)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Bibliographic Level Mode of Issuance: Monograph"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Invited Papers -- Intelligent Data Analysis in the 21st Century -- Analyzing the Localization of Retail Stores with Complex Systems Tools -- Selected Contributions 1 (Long Talks) -- Change (Detection) You Can Believe in: Finding Distributional Shifts in Data Streams -- Exploiting Data Missingness in Bayesian Network Modeling -- "]
[{"$nulls":4},"DEMScale: Large Scale MDS Accounting for a Ridge Operator and Demographic Variables -- How to Control Clustering Results? Flexible Clustering Aggregation -- Compensation of Translational Displacement in Time Series Clustering Using Cross Correlation -- Context-Based Distance Learning for Categorical Data Clustering -- Semi-supervised Text Classification Using RBF Networks -- Improving k-NN for Human Cancer Classification Using the Gene Expression Profiles -- Subgroup Discovery for Test Selection: A Novel Approach and Its Application to Breast Cancer Diagnosis -- Trajectory Voting and Classification Based on Spatiotemporal Similarity in Moving Object Databases -- Leveraging Call Center Logs for Customer Behavior Prediction -- Condensed Representation of Sequential Patterns According to Frequency-Based Measures -- ART-Based Neural Networks for Multi-label Classification -- Two-Way Grouping by One-Way Topic Models -- Selecting and Weighting Data for Building Consensus Gene Regulatory Networks -- Incremental Bayesian Network Learning for Scalable Feature Selection -- Feature Extraction and Selection from Vibration Measurements for Structural Health Monitoring -- Zero-Inflated Boosted Ensembles for Rare Event Counts -- Selected Contributions 2 (Short Talks) -- Mining the Temporal Dimension of the Information Propagation -- Adaptive Learning from Evolving Data Streams -- An Application of Intelligent Data Analysis Techniques to a Large Software Engineering Dataset -- Which Distance for the Identification and the Differentiation of Cell-Cycle Expressed Genes? -- Ontology-Driven KDD Process Composition -- Mining Frequent Gradual Itemsets from Large Databases -- Selecting Computer Architectures by Means of Control-Flow-Graph Mining -- Visualization-Driven Structural and Statistical Analysis of Turbulent Flows -- Distributed Algorithm for Computing Formal Concepts Using Map-Reduce Framework -- Multi-Optimisation Consensus Clustering -- Improving Time Series Forecasting by Discovering Frequent Episodes in Sequences -- Measure of Similarity and Compactness in Competitive Space -- Bayesian Solutions to the Label Switching Problem -- Efficient Vertical Mining of Frequent Closures and Generators -- Isotonic Classification Trees."]