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DOI: 10.14569/IJACSA.2013.040309
PDF

An Overview of Recent Machine Learning Strategies in Data Mining

Author 1: Bhanu Prakash Battula
Author 2: Dr. R. Satya Prasad

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 4 Issue 3, 2013.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Most of the existing classification techniques concentrate on learning the datasets as a single similar unit, in spite of so many differentiating attributes and complexities involved. However, traditional classification techniques, require to analysis the dataset prior to learning and for not doing so they loss their performance in terms of accuracy and AUC. To this end, many of the machine learning problems can be very easily solved just by careful observing human learning and training nature and then mimic the same in the machine learning. This paper presents an updated literature survey of current and novel machine learning strategies inducing models efficiently for supervised and unsupervised learning in data mining.

Keywords: Data mining; classification; supervised learning; unsupervised learning; learning strategies.

Bhanu Prakash Battula and Dr. R. Satya Prasad, “An Overview of Recent Machine Learning Strategies in Data Mining” International Journal of Advanced Computer Science and Applications(IJACSA), 4(3), 2013. http://dx.doi.org/10.14569/IJACSA.2013.040309

@article{Battula2013,
title = {An Overview of Recent Machine Learning Strategies in Data Mining},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2013.040309},
url = {http://dx.doi.org/10.14569/IJACSA.2013.040309},
year = {2013},
publisher = {The Science and Information Organization},
volume = {4},
number = {3},
author = {Bhanu Prakash Battula and Dr. R. Satya Prasad}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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