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Advances in Data Mining - Theoretical Aspects and Applications: 7th Industrial Conference, ICDM 2007, Leipzig, Germany, July 14-18, 2007, Proceedings de Petra Perner

Advances in Data Mining - Theoretical Aspects and Applications: 7th Industrial Conference, ICDM 2007, Leipzig, Germany, July 14-18, 2007, Proceedings: Lecture Notes in Computer Science, cartea 4597

Editat de Petra Perner
en Limba Engleză Paperback – 5 iul 2007
ICDM / MLDM Medaillie (limited edition) Meissner Porcellan, the “White Gold” of King August the Strongest of Saxonia ICDM 2007 was the seventh event in the Industrial Conference on Data Mining series and was held in Leipzig (www.data-mining-forum.de). For this edition the Program Committee received 96 submissions from 24 countries (see Fig. 1). After the peer-review process, we accepted 25 high-quality papers for oral presentation that are included in this proceedings book. The topics range from aspects of classification and prediction, clustering, Web mining, data mining in medicine, applications of data mining, time series and frequent pattern mining, and association rule mining. Germany 9,30% 4,17% China 9,30% 1,04% 6,98% 3,13% South Korea Czech Republic 6,98% 3,13% USA 6,98% 2,08% 4,65% 2,08% UK Portugal 4,65% 2,08% Iran 4,65% 2,08% India 4,65% 2,08% Brazil 4,65% 1,04% Hungary 4,65% 1,04% Mexico 4,65% 1,04% Finland 2,33% 1,04% Ireland 2,33% 1,04% Slovenia 2,33% 1,04% France 2,33% 1,04% Israel 2,33% 1,04% Spain 2,33% 1,04% Greece 2,33% 1,04% Italy 2,33% 1,04% Sweden 2,33% 1,04% Netherlands 2,33% 1,04% Malaysia 2,33% 1,04% Turkey 2,33% 1,04% Fig. 1. Distribution of papers among countries Twelve papers were selected for poster presentations that are published in the ICDM Poster Proceedings Volume.
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Specificații

ISBN-13: 9783540734345
ISBN-10: 3540734341
Pagini: 370
Ilustrații: XI, 356 p.
Dimensiuni: 155 x 235 x 24 mm
Greutate: 1.05 kg
Ediția:2007
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

Invited Talk.- Case Based Reasoning and the Search for Knowledge.- Aspects of Classification and Prediction.- Subsets More Representative Than Random Ones.- Concepts for Novelty Detection and Handling Based on a Case-Based Reasoning Process Scheme.- An Efficient Algorithm for Instance-Based Learning on Data Streams.- Softening the Margin in Discrete SVM.- Feature Selection Using Ant Colony Optimization (ACO): A New Method and Comparative Study in the Application of Face Recognition System.- Outlier Detection with Streaming Dyadic Decomposition.- VISRED –Numerical Data Mining with Linear and Nonlinear Techniques.- Clustering.- Clustering by Random Projections.- Lightweight Clustering Technique for Distributed Data Mining Applications.- Web Mining.- Predicting Page Occurrence in a Click-Stream Data: Statistical and Rule-Based Approach.- Improved IR in Cohesion Model for Link Detection System.- Improving a State-of-the-Art Named Entity Recognition System Using the World Wide Web.- Data Mining in Medicine.- ISOR-2: A Case-Based Reasoning System to Explain Exceptional Dialysis Patients.- The Role of Prototypical Cases in Biomedical Case-Based Reasoning.- Applications of Data Mining.- A Search Space Reduction Methodology for Large Databases: A Case Study.- Combining Traditional and Neural-Based Techniques for Ink Feed Control in a Newspaper Printing Press.- Active Learning Strategies: A Case Study for Detection of Emotions in Speech.- Neural Business Control System.- A Framework for Discovering and Analyzing Changing Customer Segments.- Collaborative Filtering Using Electrical Resistance Network Models.- Visual Query and Exploration System for Temporal Relational Database.- Towards an Online Image-Based Tree Taxonomy.- Distributed Generative Data Mining.- Time Series andFrequent Pattern Mining.- Privacy-Preserving Discovery of Frequent Patterns in Time Series.- Efficient Non Linear Time Series Prediction Using Non Linear Signal Analysis and Neural Networks in Chaotic Diode Resonator Circuits.- Association Minnig.- Using Disjunctions in Association Mining.