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Trends in Computational Mathematics and Statistics
Submission Deadline: Nov. 20, 2015
Lead Guest Editor
K. Anitha
Department Of Mathematics, S.A.Engineering College, Anna University, Chennai, Tamil Nadu, India
Guest Editor
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Guidelines for Submission
Manuscripts can be submitted until the expiry of the deadline. Submissions must be previously unpublished and may not be under consideration elsewhere.
Papers should be formatted according to the guidelines for authors (see: http://www.sciencepublishinggroup.com/journal/guideforauthors?journalid=149). By submitting your manuscripts to the special issue, you are acknowledging that you accept the rules established for publication of manuscripts, including agreement to pay the Article Processing Charges for the manuscripts. Manuscripts should be submitted electronically through the online manuscript submission system at http://www.sciencepublishinggroup.com/login. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal and will be listed together on the special issue website.
Published Papers
The special issue currently is open for paper submission. Potential authors are humbly requested to submit an electronic copy of their complete manuscript by clicking here.
Data Mining is the process of extracting or mining, analyzing the information from large data base for discovering new knowledge that can be translated into many applications. Practical issues such as handling noisy and incomplete data are new challenges faced by researchers in the field of Bioinformatics, Web and Text mining, Signal and Image/Medical Image Processing and Multimedia. This special issue is to overcome the above issues by data reduction through soft computing without information loss. Original research papers are solicited in any aspect of data mining techniques using Rough Sets, Fuzzy Sets, Evolutionary Algorithm, Genetic Algorithm, Artificial Intelligence and Near Sets.

Aims and Scope:

1. Soft computing data mining techniques
2. Biological Data mining
3. Rough Sets
4. Artificial Intelligence
5. Neural/Artificial Neural Network
6. To facilitate new data mining algorithm
7. To derive comprehensive machine learning algorithms for high dimensional databases.
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