Unsupervised Attribute Reduction Algorithms for Multiset-Valued Data Based on Uncertainty Measurement
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 <inl...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/11/1718 |
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| Summary: | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>θ</mi></semantics></math></inline-formula>-tolerance relation to form the information granules. Then, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>θ</mi></semantics></math></inline-formula>-information entropy and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>θ</mi></semantics></math></inline-formula>-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. |
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| ISSN: | 2227-7390 |