Missing Value Imputation Based on Gaussian Mixture Model for the Internet of Things Article Swipe
YOU?
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· 2015
· Open Access
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· DOI: https://doi.org/10.1155/2015/548605
This paper addresses missing value imputation for the Internet of Things (IoT). Nowadays, the IoT has been used widely and commonly by a variety of domains, such as transportation and logistics domain and healthcare domain. However, missing values are very common in the IoT for a variety of reasons, which results in the fact that the experimental data are incomplete. As a result of this, some work, which is related to the data of the IoT, can’t be carried out normally. And it leads to the reduction in the accuracy and reliability of the data analysis results. This paper, for the characteristics of the data itself and the features of missing data in IoT, divides the missing data into three types and defines three corresponding missing value imputation problems. Then, we propose three new models to solve the corresponding problems, and they are model of missing value imputation based on context and linear mean (MCL), model of missing value imputation based on binary search (MBS), and model of missing value imputation based on Gaussian mixture model (MGI). Experimental results showed that the three models can improve the accuracy, reliability, and stability of missing value imputation greatly and effectively.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2015/548605
- https://downloads.hindawi.com/journals/mpe/2015/548605.pdf
- OA Status
- hybrid
- Cited By
- 67
- References
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W1978695314
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W1978695314Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1155/2015/548605Digital Object Identifier
- Title
-
Missing Value Imputation Based on Gaussian Mixture Model for the Internet of ThingsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2015Year of publication
- Publication date
-
2015-01-01Full publication date if available
- Authors
-
Xiaobo Yan, Weiqing Xiong, Liang Hu, Feng Wang, Kuo ZhaoList of authors in order
- Landing page
-
https://doi.org/10.1155/2015/548605Publisher landing page
- PDF URL
-
https://downloads.hindawi.com/journals/mpe/2015/548605.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://downloads.hindawi.com/journals/mpe/2015/548605.pdfDirect OA link when available
- Concepts
-
Missing data, Imputation (statistics), Computer science, Data mining, Gaussian, Mixture model, Internet of Things, Statistics, Artificial intelligence, Mathematics, Machine learning, Embedded system, Quantum mechanics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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67Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6, 2024: 4, 2023: 7, 2022: 7, 2021: 11Per-year citation counts (last 5 years)
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18Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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