International E-publication: Publish Projects, Dissertation, Theses, Books, Souvenir, Conference Proceeding with ISBN.  International E-Bulletin: Information/News regarding: Academics and Research

Self Organized Map Network for Classification of Multilevel Data

Author Affiliations

  • 1Department of Computer Applications, Maulana Azad National Institute of Technology, Bhopal, 462-052, INDIA
  • 2Department of Computer Applications, Maulana Azad National Institute of Technology, Bhopal, 462-052, INDIA
  • 3Department of Computer Applications, Maulana Azad National Institute of Technology, Bhopal, 462-052, INDIA

Res. J. Computer & IT Sci., Volume 3, Issue (1), Pages 1-4, March,20 (2015)

Abstract

Data Mining is extraction of hidden knowledge from large set of data. Classification is a very important technique of data mining. Associative classification is a form of classification in which classification and association techniques are merged. From associative classification we can find more important rules and more accurate classification results. This paper presents an approach for classification of multilevel data using Self Organized Map Network. It is a kind of Artificial Neural Network. This approach gives a new kind of classification. It’s importance can be felt in such applications in which data is sparsed and stored in different abstraction levels.

References

  1. Han J. and Kamber M., Data Mining: Concepts andTechniques, Morgan Kaufmann Publishers, (2006)
  2. Pujari A.K., Data Mining Techniques, Universities Press(India) Private Limited, (2001)
  3. Stonebraker M., Agrawal R., Dayal U., Neuhold E.J. andReuter A., DBMS Research at a Crossroads: The ViennaUpdate, Proc. 19th very large data bases conf., 688-692(1993)
  4. Kothari A., Keskar A., Chalasani R., Srinath S., RoughNeuron Based Neural Classifier, Emerging Trends inEngineering and Technology, 2008, ICETET
  5. Mishra M. and Behera H.S., Kohonen Self OrganizingMap with Modified K-means clustering For HighDimensional Data Set, International Journal of AppliedInformation Systems (IJAIS), Foundation of ComputerScience FCS, New York, USA, 2(3), May, (2012)
  6. Paigwar S. and Shukla S., Neural Network Based OfflineSignature Recognition and Verification System, ResearchJournal of Engineering Sciences, 2(2), 11-15, February,(2013)
  7. Gangwar R.P., Agrawal J. and Sharma V., Auto-LabelThreshold Generation for Multiple RelationalClassifications based on SOM Network, IJCA, 40(7),(2012)
  8. Del-Hoyo R., Buldain D. and Marco A., SupervisedClassification with Associative SOM, ComputationalMethods in Neural Modeling, Lecture Notes in ComputerScience, 2686, 334-341, (2003)
  9. Liu B., Hsu W. and Ma Y., Integrating classification andassociation rule mining, In Knowledge Discovery and DataMining, 80–86, (1998)