A Local Density Approach for Unsupervised Feature Discretization

Authors:
Shengyi Jiang;Wen Yu
Affiliations:
School of Informatics, Guangdong University of Foreign Studies, Guangzhou 510006;School of Informatics, Guangdong University of Foreign Studies, Guangzhou 510006
Venue:
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Year:
2009

Citing 2
Cited 0

Feature Selection via Discretization

IEEE Transactions on Knowledge and Data Engineering
A Modified Chi2 Algorithm for Discretization

IEEE Transactions on Knowledge and Data Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

Discretization is an important preprocess in data mining tasks. Considering the density distribution of attributes, this paper proposes a novel discretization approach. The time complexity is O (m *n * logn ) as EW and PKID, so it can scale to large datasets. We use the datasets from the UCI repository to perform the experiments and compare the effects with some current discretization methods; the experimental results demonstrate that our method is effective and practicable.