Vol 5 No 7 (2019): IJRDO - Journal of Computer Science Engineering | ISSN: 2456-1843
Articles

Concept Lattice Theory in Data Mining and its Applications

KANINDA MUSUMBU
Université Nouveaux Horizons
Bio
Pascal SUNGU
Bio
Nathalie WANDJI
African Institute of Mathematical Science
Bio
Published July 13, 2019
Keywords
  • Formal concept,
  • Frequent pattern,
  • Association rules
How to Cite
MUSUMBU, K., SUNGU, P., & WANDJI, N. (2019). Concept Lattice Theory in Data Mining and its Applications. IJRDO - Journal of Computer Science Engineering (ISSN: 2456-1843), 5(7), 01-10. Retrieved from https://ijrdo.org/index.php/cse/article/view/2951

Abstract

Concept lattice has been proven to be a very eective tool and architecture for data mining in general. It is widely used for data analysis and knowledge discovery and various concept lattice based approaches are used depending on the type of data. This paper aims at presenting one application of the lattice theory : the text mining. In this approach, we applied the notion of lattice theory by using one of its components mostly used in data mining, the formal concept analysis which has a powerful method, the association rule extraction which helps to nd in a database patterns which appear frequently together.

Downloads

Download data is not yet available.

References

  1. K. Bertet ; Structure de treillis: contributions structurelles et algorithmiques: quelques usages pour des données images; 2010.
  2. K.I. Ignatov , Introduction to formal concept analysis and its applications in information retrieval and related fields; Russian Summer School
  3. in Information Retrieval; 42–141; Springer; 2014.
  4. Zhao, Qiankun, Bhowmick and Sourav. ; Association rule mining: A survey; Nanyang Technological University, Singapore; 2003.
  5. Masseglia, Florent and Poncelet, Pascal and Teisseire, Maguelonne. ; Successes and new directions in data mining; IGI Global; 2008.
  6. Zhang, Chengqi and Zhang, Shichao. ; Association rule mining: models and algorithms, Springer-Verlag; 2002.
  7. Cherfi, Hacene and Toussaint, Yannick. ; Adéquation d’indices statistiques à l’interprétation de règles d’association; 6èmes Journées internationales d’Analyse statistique des Données Textuelles-JADT 2002; 233–244; 2002.
  8. G. Grätzer ; General lattice theory; Springer Science & Business Media; 2002.