• DOI:10.1021/acs.jcim.5b00198
Corpus ID: 23011463

Perspectives on Knowledge Discovery Algorithms Recently Introduced in Chemoinformatics: Rough Set Theory, Association Rule Mining, Emerging Patterns, and Formal Concept Analysis

@article{Gardiner2015PerspectivesOK,
  title={Perspectives on Knowledge Discovery Algorithms Recently Introduced in Chemoinformatics: Rough Set Theory, Association Rule Mining, Emerging Patterns, and Formal Concept Analysis},
  author={E. Gardiner and V. J. Gillet},
  journal={Journal of chemical information and modeling},
  year={2015},
  volume={55 9},
  pages={
          1781-803
        }
}

E. Gardiner, V. J. Gillet

Published 2015

Computer Science, Medicine

Journal of chemical information and modeling

Knowledge Discovery in Databases (KDD) refers to the use of methodologies from machine learning, pattern recognition, statistics, and other fields to extract knowledge from large collections of data, where the knowledge is not explicitly available as part of the database structure. In this paper, we describe four modern data mining techniques, Rough Set Theory (RST), Association Rule Mining (ARM), Emerging Pattern Mining (EP), and Formal Concept Analysis (FCA), and we have attempted to give an… CONTINUE READING

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