A growing attention has been paid to the study, development and application of data mining. As a result there is an urgent need for sophisticated techniques and tools that can handle new fields of data mining, e.g. spatial data mining in the context of spatial-temporal characteristics, streaming data mining, and biomedical data mining. Our knowledge on data mining should also have to be expanded to new applications. The International Conference on Advanced Data Mining and Applications (ADMA2010) aims at bringing together the experts on data mining in the world, and provides a leading international 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.

The 1st International Conference on Advanced Data Mining and Applications (ADMA 2005) was successfully held in Wuhan , China , and the proceedings were published by Springer in LNAI 3584. The 2nd International Conference on Advanced Data Mining and Applications (ADMA 2006) was held in Xi'An , China , and the proceedings were also published by Springer in LNAI 4093. The 3rd ADMA 2007 was sponsored by Harbin Institute of Technology, and the proceedings were published by Springer in LNAI 4632. The 4th ADMA 2008 was held by Sichuan University, and the proceedings were published by Springer in LNAI 5139. The 5th ADMA 2009 was held by Beijing Normal University, and the proceedings were published by Springer in LNAI 5678.

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

Paper Submission

Paper submission will be electronic through the submission system.

Before submitting your manuscripts, please read the paper format seriously.