Revisiting Numerical Pattern Mining with Formal Concept Analysis
Abstract
We investigate the problem of mining numerical data with Formal Concept Analysis. The usual way is to use a scaling procedure —transforming numerical attributes into binary ones — leading either to a loss of information or of efficiency, in particular w.r.t. the volume of extracted patterns. By contrast, we propose to directly work on numerical data in a more precise and efficient way. For that, the notions of closed patterns, generators and equivalent classes are revisited in the numerical context. Moreover, two original algorithms are proposed and tested in an evaluation involving real-world data, showing the quality of the present approach.
Cite
Text
Kaytoue et al. "Revisiting Numerical Pattern Mining with Formal Concept Analysis." International Joint Conference on Artificial Intelligence, 2011. doi:10.5591/978-1-57735-516-8/IJCAI11-227Markdown
[Kaytoue et al. "Revisiting Numerical Pattern Mining with Formal Concept Analysis." International Joint Conference on Artificial Intelligence, 2011.](https://mlanthology.org/ijcai/2011/kaytoue2011ijcai-revisiting/) doi:10.5591/978-1-57735-516-8/IJCAI11-227BibTeX
@inproceedings{kaytoue2011ijcai-revisiting,
title = {{Revisiting Numerical Pattern Mining with Formal Concept Analysis}},
author = {Kaytoue, Mehdi and Kuznetsov, Sergei O. and Napoli, Amedeo},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2011},
pages = {1342-1347},
doi = {10.5591/978-1-57735-516-8/IJCAI11-227},
url = {https://mlanthology.org/ijcai/2011/kaytoue2011ijcai-revisiting/}
}