The 2006 IEEE International Conference on Data Mining
18 - 22 December 2006, Hong Kong
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Topics related to the design, analysis and implementation of data mining theory, systems and applications are of interest. These include, but are not limited to the following areas:

• Foundations of data mining
•  Data mining and machine learning algorithms and methods in traditional areas (such as classification, regression, clustering, probabilistic modeling, and association analysis), and in new areas
• Mining text and semi-structured data, and mining temporal, spatial and multimedia data
• Mining data streams
• Pattern recognition and trend analysis
• Collaborative filtering/personalization
• Data and knowledge representation for data mining
•  Query languages and user interfaces for mining
• Complexity, efficiency, and scalability issues in data mining
• Data pre-processing, data reduction, feature selection and feature transformation
• Post-processing of data mining results
• Statistics and probability in large-scale data mining
•  Soft computing (including neural networks, fuzzy logic, evolutionary computation, and rough sets) and uncertainty management for data mining
• Integration of data warehousing, OLAP and data mining
• Human-machine interaction and visual data mining
• High performance and parallel/distributed data mining
• Quality assessment and interestingness metrics of data mining results
•  Security, privacy and social impact of data mining
•  Data mining applications in bioinformatics, electronic commerce, Web, intrusion detection, finance, marketing, healthcare, telecommunications and other fields

Last update: 16 Dec 2019 top

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