Unsupervised feature selection in binarization of real attributes for conceptual clustering
This paper proposes an approach for processing noisy data to form homogeneous subgroups of objects based on Formal Concept Analysis (FCA). The approach involves binary encoding of heterogeneous features and unsupervised feature selection using the Laplacian Score. The selected feature set is then us...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
|
| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/03/itmconf_hmmocs-III2024_04004.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|