Home            Contact us            FAQs
    
      Journal Home      |      Aim & Scope     |     Author(s) Information      |      Editorial Board      |      MSP Download Statistics

     Research Journal of Applied Sciences, Engineering and Technology


Expertised String Mining in Outsized Databases and Hefty Files

K. Geetha Rani, Shobhanjaly P. Nair, P. Visu and S. Koteeswaran
Department of Computer Science and Engineering, Vel Tech. Dr. RR and Dr. SR Technical University, Chennai, Tamil Nadu, India
Research Journal of Applied Sciences, Engineering and Technology  2014  23:5063-5067
http://dx.doi.org/10.19026/rjaset.7.900  |  © The Author(s) 2014
Received: February 22, 2014  |  Accepted: April ‎09, ‎2014  |  Published: June 20, 2014

Abstract

In the last few decades data mining is one of the important research areas in data maintenance of computing. In computing world, plentiful algorithms are proposed for mining. The few applications of data mining are web mining, video mining, knowledge mining and string mining. In these applications, string mining is focused and concentrated to overcome the space allocation process. To overcome the drawback suffered by the space allocation process Efficient String Mining (ESM) algorithm is proposed. The ESM algorithm performs operation faster, helps in reducing the space allocation, which in turn improves the performance of string mining and it is also possible to locate patterns that recurrent in the string database or file with a given support.

Keywords:

Data mining, string mining, suffix array, suffix tree, text mining,


References

  1. Adrian, K. and O. Enno, 2008. A space efficient solution to the frequent string mining problem for many databases. Data Min. Knowl. Disc., 17(1): 24-38.
    CrossRef    
  2. David, W. and H.S. Marcel, 2008. Efficient string mining under constraints via the deferred frequency index. Proceedings of the 8th Industrial Conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing and Theoretical Aspects (ICDM '08), pp: 374-388.
  3. Fischer, J., V. Huen and S. Kramer, 2006. Optimal string mining under frequency constraints. Proceeding of the European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), 4213: 139-150.
    CrossRef    
  4. Hiroki, A., W. Atsushi, F. Ryoichi and A. Setsuo, 1998. An efficient algorithm for text data mining with optimal string patterns. Proceeding of the ALT'98, LNAI, Vol. 247.
  5. Jasbir, D., J.P. Simon and T. Andrew, 2012a. Practical efficient string mining. IEEE T. Knowl. Data En., 24(4).
  6. Jasbir, D., J.P. Simon and T. Andrew, 2012b. Trends in suffix sorting: A survey of low memory algorithms. Proceedings of the 35th Australasian Computer Science Conference (ACSC'12).
  7. Johannes, F., H. Volker and K. Stefan, 2005. Fast frequent string mining using suffix arrays. Proceedings of the 5th IEEE International Conference on Data Mining (ICDM '05), pp: 609-612.
  8. Koteeswaran, S. and E. Kannan, 2013. Analysis of Bilateral Intelligence (ABI) for textual pattern learning. Inform. Technol. J., 12(4): 867-870.
    CrossRef    
  9. Koteeswaran, S., P. Visu and J. Janet, 2012a. A review on clustering and outlier analysis techniques in data mining. Am. J. Appl. Sci., 9(2): 254-258.
    CrossRef    
  10. Koteeswaran, S., J. Janet and E. Kannan, 2012b. Significant term list based metadata conceptual mining model for effective text clustering. J. Comput. Sci., 8(10): 1660-1666.
    CrossRef    
  11. Mocian, H., 2012. Applications of string mining techniques in text analysis. Sci. Bull. Petru Maior Univ., Targu Mures, 9(1): 5.
  12. Yun, C., Y. Yirong and R.R. Muntz, 2003. Indexing and mining free trees. Proceeding of the 3rd IEEE International Conference on Data Mining (ICDM, 2003), pp: 509-512.
  13. Zhan, X.G., X.M. Zhi, S.X. Yu and L. Li, 2010. An Optimized LCP table based algorithm for frequent string mining. Appl. Mech. Mater., 20-23: 653-658.
    CrossRef    

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
Submit Manuscript
   Information
   Sales & Services
Home   |  Contact us   |  About us   |  Privacy Policy
Copyright © 2024. MAXWELL Scientific Publication Corp., All rights reserved