Local Similarity Imputation Based on Fast Clustering for Incomplete Data in Cyber-Physical Systems

Zhao, Liang; Chen, Zhikui; Yang, Z.; Hu, Yueming; Obaidat, Mohammad S.

doi:10.1109/jsyst.2016.2576026

Cited by 42 publications

(26 citation statements)

References 24 publications

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“…To use this algorithm, it is necessary to choose the optimal K and define a distance measurement between two observations. A local similarity imputation based on Fast Clustering was proposed in Zhao, Chen, Yang, Hu, and Obaidat (2018) . The authors partition the incomplete data with a fast clustering method (Stacked Autoencoder-based), then fill the missing data within each cluster using a KNN algorithm.…”

Section: Single Imputation Methodsmentioning

confidence: 99%

Model selection to improve multiple imputation for handling high rate missingness in a water quality dataset

Ratolojanahary

Ngouna

Medjaher

et al. 2019

Expert Systems with Applications

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Section: Single Imputation Methodsmentioning

confidence: 99%

Model selection to improve multiple imputation for handling high rate missingness in a water quality dataset

Ratolojanahary

Ngouna

Medjaher

et al. 2019

Expert Systems with Applications

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“…The evidence chain approach [24] was also applied to mine all relevant evidence of missing values and build the further estimation of missing values. Moreover, Zhao et al [25] developed a novel local similarity imputation method that estimates missing data based on fast clustering and top k-nearest neighbors, and in order to improve the imputation accuracy, a two-layer stacked autoencoder combined with distinctive imputation is applied to locate the principal features of a dataset for clustering. Tsai et al [26] introduced a class center based missing value imputation approach to produce effective imputation results more efficiently based on measuring the class center of each class and then the distances between it and the other observed data are used to define a threshold for the later imputation.…”

Section: Related Workmentioning

confidence: 99%

Data-driven missing data imputation in cluster monitoring system based on deep neural network

Lin

Алам

et al. 2019

Appl Intell

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Due to cluster instability, not in the cluster monitoring system. This paper focuses on the missing data imputation processing for the cluster monitoring application and proposes a new hybrid multiple imputation framework. This new imputation approach is different from the conventional multiple imputation technologies in the fact that it attempts to impute the missing data for an arbitrary missing pattern with a model-based and data-driven combination architecture. Essentially, the deep neural network, as the data model, extracts deep features from the data and deep features are further calculated then by a regression or data-driven strategies and used to create the estimation of missing data with the arbitrary missing pattern. This paper gives evidence that if we can train a deep neural network to construct the deep features of the data, imputation based on deep features is better than that directly on the original data. In the experiments, we compare the proposed method with other conventional multiple imputation approaches for varying missing data patterns, missing ratios, and different datasets including real cluster data. The result illustrates that when data encounters larger missing ratio and various missing patterns, the proposed algorithm has the ability to achieve more accurate and stable imputation performance. Keywords Missing data • Cluster monitoring • Deep belief networks • Multiple imputation 1 Introduction Clusters not only making an accurate decision but also monitoring with partial missing cluster data is the practical problem. Systems are becoming increasingly complex due to the large number of services and resources. The cluster monitoring system is what we use in a systematic way to process or analyze cluster data at a remote location under normal circumstances.

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“…With a larger τ , the algorithm runs fast but the selected positions may have worse utilities; With a smaller τ , the algorithm runs slow but the selected positions have better utilities. We will consider using energy efficient clustering algorithms [20], [21], [22] to find the deployment positions in our future work.…”

Section: Algorithm 1 the Deployment Algorithm Inputmentioning

confidence: 99%

Deploying Edge Computing Nodes for Large-Scale IoT: A Diversity Aware Approach

Zhao

Min

Gao

et al. 2018

IEEE Internet Things J.

113

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The recent advances in microelectronics and communications have led to the development of large-scale IoT networks, where tremendous sensory data is generated and needs to be processed. To support real-time processing for large-scale IoT, deploying edge servers with storage and computational capability is a promising approach. In this paper, we carefully analyze the impacting factors and key challenges for edge node deployment. We then propose a novel three-phase deployment approach which considers both traffic diversity and the wireless diversity of IoT. The proposed work aims at providing real-time processing service for the IoT network and reducing the required number of edge nodes. We conducted extensive simulation experiments, the results show that compared to the existing works that overlooked the two kinds of diversities, the proposed work greatly reduces the number of edge nodes and improves the throughput between IoT and edge nodes.

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Local Similarity Imputation Based on Fast Clustering for Incomplete Data in Cyber-Physical Systems

Cited by 42 publications

References 24 publications

Model selection to improve multiple imputation for handling high rate missingness in a water quality dataset

Model selection to improve multiple imputation for handling high rate missingness in a water quality dataset

Data-driven missing data imputation in cluster monitoring system based on deep neural network

Deploying Edge Computing Nodes for Large-Scale IoT: A Diversity Aware Approach

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