Data mining is an important tool in science, engineering, industrial processes, healthcare, business, and medicine. The datasets in these fields are large, complex, and often noisy. Extracting knowledge requires the use of sophisticated, high–performance and principled analysis techniques and algorithms, based on sound theoretical and statistical foundations. These techniques in turn require powerful visualization technologies; implementations that must be carefully tuned for performance; software systems that are usable by scientists, engineers, and physicians as well as researchers; and infrastructures that support them. <b>Keywords:</b> Methods and Algorithms<br>Classification; Clustering; Frequent Pattern Mining; Probabilistic and Statistical Methods; Spatial and Temporal Mining ; Data Stream Mining ; Abnormality and Outlier Detection; Feature Selection / Feature Extraction; Dimension Reduction; Data Reduction; Mining with Constraints; Data Cleaning and Noise Reduction ; Computational Learning Theory; Multi–Task Learning; Adaptive Algorithms ; Scalable and High–Performance Mining; Mining Graphs ; Mining Semistructured Data; Mining Complex Datasets; Mining on Emerging Architectures; Text and Web Mining;<br>Applications<br>Astronomy & Astrophysics; High Energy Physics; Collaborative Filtering; Earth Science; Risk Management; Supply Chain Management; Customer Relationship Management; Finance; Genomics and Bioinformatics; Drug Discovery; Healthcare Management; Automation & Process Control; Logistics Management; Intrusion and Fraud detection; Intelligence Analysis; Biosurveillance; Sensor Network Applications; Social Network Analysis; Application Case Studies; Other Novel Applications;<br>Human Factors and Social Issues<br>Ethics of Data Mining; Intellectual Ownership; Privacy Models; Privacy Preservation Techniques; Risk Analysis; User Interfaces; Interestingness and Relevance; Data and Result Visualization
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SDM
City
Atlanta
Country
United States
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