27 June 2012

Call for papers: Advances in Data Mining and Machine Learning

A special issue of International Journal of Intelligent Systems Technologies and Applications

Over the past few years, research and development in data mining has made great progress. Many successful applications have been reported in journals and conferences. In general, data mining systems typically help businesses to expose previously unknown patterns in their databases. It has now been recognised that mining for information and knowledge from large databases and documents will be the next revolution in database systems. It is considered very important for major cost savings and potential revenue increase with immediate applications in business, decision systems, information management, communication, scientific research and technology development. Since data mining is still a relatively new research field and a great deal of research and applications are still in progress.

Increasingly sophisticated techniques, such as association rules, fuzzy data mining, genetic algorithms applied to data mining, utility mining etc. have been proposed and many of them have already been deployed in many real-world applications. The major goal of this special issue is to bring together the researchers in the data mining field to illustrate its pressing actual needs, demonstrate challenging research issues and exchange the state-of-the-art research and development.

The issue aims to publish the best full papers from the Special Session on Advanced Data Mining Techniques and Applications (ADMTA'12) in 4th International Conference on Computational Collective Intelligence 2012 (ICCCI’12), with a special focus on data mining. ADMTA'12 aims to bring together researchers and practitioners in the field of data mining from around the world to discuss their studies.

Additionally, the call is also opened to researchers unable to attend the ADMTA’12 to publish high quality, state-of-the-art research.

Suitable topics include but are not limited to:
  • Classification systems
  • Association rules
  • Clustering
  • Utility mining
  • Stream mining
  • Temporal mining
  • Spatial mining
  • Features selection for data mining and machine learning
  • Rough sets applied to data mining and machine learning
  • Fuzzy sets applied to data mining and machine learning
  • Genetic algorithms applied to data mining and machine learning
  • Neural networks applied to data mining and machine learning
  • Applications of data mining and machine learning
  • Soft sets applied to data mining and machine learning
Important Dates
Paper submission: 31 December, 2012
Notification to authors: 31 March, 2013
Final version submission: 30 April, 2013


TaskTrek said...

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loginworks said...

Data mining is also defined as an automated mining of hidden data from enormous databases used for predictive analysis. This process sometimes requires the use of statistical techniques and mathematical algorithms that can be integrated with other software tools. This process includes different technical approaches such as: Clustering, Data Summarization..

Data Mining Technology Application in Business