LNAI 5139 (for ADMA 2008)

The Fourth International Conference on

Advanced Data Mining and Applications (ADMA 2008)

 

Important Notice:
1. A Best Paper and a Best Student Paper will be awarded at the conference.
2. Some accepted papers will be expanded and re-reviewed to be published in a Special Issue of Computational Intelligence Journal (SCI Indexed) and Journal of Frontiers of Computer Science and Technology.

3. The Proceedings of ADMA 2007 were published by Springer in its Lecture Notes in Computer Science, and were indexed by EI. Proceedings of ADMA 2008 will be published by the same publisher, and expected to be included in EI.

Chengdu , also known as "Rong City"," Paradise Place", is the capital of Sichuan Province , one of the historical and cultural cities in China , a city full of exotic atmosphere, a city of glittering and translucence.

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 2 nd 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 3 rd ADMA 2007 was sponsored by Harbin Institute of Technology, and the proceedings were published by Springer in LNAI 4632.

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 4 th International Conference on Advanced Data Mining and Applications (ADMA2008) 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.

Submission Instructions:
1. Manuscripts should be submitted electronically to http://cs.scu.edu.cn/~adma08
2. The paper should be in English and contain unpublished contributions to the data mining fields. Papers at the conference will be accepted in two categories:
      - Regular papers (up to 12 pages in the proceedings)
      - Short papers (up to 8 pages in the proceedings)
3. The format should follow Lecturer Notes in Computer Science. The submission of papers must be in PDF. The Authors should exclude the usage of non-English fonts to avoid the problems from reviewers' machines. All accepted papers must strictly follow the LNCS/LNAI Authors instructions. The final Camera-Ready copies of the accepted papers must be uploaded online strictly before the deadline.

For more info: please mail Charles Li at charles.li2004@gmail.com

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

Advanced Data Mining Topics

 

  • Grand challenges of data mining
  • Parallel and distributed data mining algorithms
  • Mining on data streams
  • Graph and subgraph mining
  • Spatial data mining
  • Text, video, multimedia data mining
  • Web mining
  • High performance data mining algorithms
  • Correlation mining
  • Bench marking and evaluations
  • Interactive data mining
  • Data-mining-ready structures and pre-processing
  • Data mining visualization
  • Information hiding in data mining
  • Security and privacy issues
  • Competitive analysis of mining algorithms

Data Mining Applications (applied data mining in following listed areas)

  • Database administration, indexing, performance tuning
  • Grid computing
  • DNA Sequencing, Bioinformatics, Genomics, and biometrics
  • Image interpretations
  • E-commerce and Web services
  • Medical informatics
  • Disaster prediction
  • Remote monitoring
  • Financial market analysis
  • Online filtering