Unsupervised Attribute Reduction Algorithms for Multiset-Valued Data Based on Uncertainty Measurement
Year: 2025
Type: article
Abstract: Missing data introduce uncertainty in data mining, but existing set-valued approaches ignore frequency information. We propose unsupervised attribute reduction algorithms for multiset-valued data to address this gap. First, we define a multiset-valued information system (MSVIS) and establish θ-tolerance relation to form the information granules. Then, θ-information entropy and θ-information amount are introduced as uncertainty measures. Finally, these two UMs are used to design two unsupervised attribute reduction algorithms in an MSVIS. The experimental results demonstrate the superiority of the proposed algorithms, achieving average reductions of 50% in attribute subsets while improving clustering accuracy and outlier detection performance. Parameter analysis further validates the robustness of the framework under varying missing rates.
Source: Mathematics
DOI: 10.3390/math13111718
Authors: Xiaoyan Guo, Yichun Peng, Yu Li, Hai Lin
Language: English
Topic: Rough Sets and Fuzzy Logic
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4410628769