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ADMA 2010, Full paper submission due: July 20, 201 박명호



Full paper submission due: July 20, 2010

Acceptance notification: August 20, 2010

Early registration deadline: September 13, 2010

Final Camera-ready: October 3, 2010

Conference dates: November 19-21, 2010


The International Conference on Advanced Data Mining and Applications (ADMA2010), to be held in Chongqing from November 19 to 21, 2010 , will be a prestigious and high quality international conference that establishes a forum for the dissemination of original research results in data mining, spanning applications, algorithms, software and systems, and different applied disciplines with potential in data mining.

Chongqing is famous for the towering mountains and roaring rivers, which have witnessed the local civilization of over 3000 years. The central urban area of Chongqing, or Chongqing proper, is a beautiful city with its unique features. Built on mountains and embraced by the Yangtze and Jialing rivers, it is known as a "mountain city" and a "city on rivers". The night scene of the city is very charming, with millions of lights and their reflection on the rivers, forming another Milky Way.
Chongqing University is a nationally famed comprehensive key university in China, directly under the State Ministry of Education, also a university listed among the first group of "211 Project" universities gaining preferential support in their construction and development from the Central Government of China.

The proceedings of the conference (ADMA2010) will be published by Springer in its Lecture Notes in Computer Science series, and indexed by EI. A selected number of the accepted papers will be expanded and revised for possible inclusion in International Journal on Wavelet, Multiresolution, and Information Processing: An International Journal , which could be indexed by several databases including SCI expanded.

We invite authors to submit papers on any topics of advanced data mining and applications, including but not limited to:

    Data mining foundations
  • 1. Novel data mining algorithms in traditional areas (such as classification, regression, clustering, probabilistic modeling, and association analysis)
  • 2. Algorithms for new, structured, data types, such as arising in chemistry, biology, environment, and other scientific domains
  • 3. Developing a unifying theory of data mining
  • 4. Mining sequences and sequential data
  • 5. Mining spatial and temporal datasets
  • 6. Mining textual , Semistructured and unstructured datasets
  • 7. High performance implementations of data mining algorithms
  • 8. Scalable and High-Performance Mining
  • 9. Frequent Pattern Mining
  • 10. Probabilistic and Statistical Methods
  • 11. Mining with Constraints
  • 12. Computational Learning Theory
  • 13. Multi-Task Learning
  • 14. Adaptive Algorithms
  • 15. Abnormality and Outlier Detection
  • 16. Data Stream Mining
  • 17. Spatial and Temporal Mining
  • 18. Mining Graphs
  • 19. Mining Complex Datasets
  • 20. Mining on Emerging Architectures
  • 21. Text and Web Mining
  • 22. Other Novel Methods

    Mining in targeted application contexts
  • 1. Mining high speed data streams
  • 2. Mining sensor data
  • 3. Distributed data mining and mining multi-agent data
  • 4. Mining in networked settings: web, social and computer networks, and online communities
  • 5. Data mining in electronic commerce, such as recommendation, sponsored web search, advertising, and marketing tasks

    Methodological aspects and the KDD process
  • 1. Data pre-processing, Dimension Reduction, data reduction, feature selection, Data Cleaning, Noise Reduction, and feature transformation
  • 2. Quality assessment, interestingness analysis, and post-processing
  • 3. Statistical foundations for robust and scalable data mining
  • 4. Handling imbalanced data
  • 5. Automating the mining process and other process related issues
  • 6. Dealing with cost sensitive data and loss models
  • 7. Human-machine interaction and visual data mining
  • 8. Security, privacy, and data integrity

    Integrated KDD applications and systems
  • 1. Bioinformatics, Life Sciences, Medical Systems , computational chemistry, geoinformatics, and other science & engineering disciplines
  • 2. Content Management, Digital Libraries, and Search
  • 3. Image and Graphics
  • 4. Complex system
  • 5. Cloud computing and Grid computing
  • 6. Wireless Sensor Networks
  • 7. Evolutionary Computation
  • 8. Ethics of Data Mining
  • 9. Intellectual Ownership
  • 10. Privacy Models
  • 11. Privacy Preserving Data Mining and Data Publishing
  • 12. Risk Analysis
  • 13. User Interfaces
  • 14. Interestingness and Relevance
  • 15. Data and Result Visualization
  • 16. Engineering and Manufacturing
  • 17. Enterprise data mining and management
  • 18. Geographic and Environmental Information Systems
  • 19. Computational finance, online trading, and analysis of markets
  • 20. Intrusion detection, fraud prevention, and surveillance
  • 21. Healthcare, epidemic modeling, and clinical research
  • 22. Customer relationship management
  • 23. Enterprise resource planning
  • 24. Government , home and personal applications
  • 25. Scientific databases
  • 26. Social network analysis
  • 27. Intelligence analysis
  • 28. Other novel applications and case studies
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