Clustering‐Based Multiple Imputation via Gray Relational Analysis for Missing Data and Its Application to Aerospace Field

Tian, Jing; Yu, Bing; Yu, Dan; Ma, Shilong

doi:10.1155/2013/720392

Cited by 11 publications

(5 citation statements)

References 29 publications

(50 reference statements)

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“…A penalized dissimilarity measure‐based approach has also been proposed (Datta, Bhattacharjee, & Das, 2018). In nonbiological settings, clustering‐based imputation via partitioning of the original data into two nonoverlapping subsets, that is, the missing values subset and the complete values subset, has been endorsed (Tian, Yu, Yu, & Ma, 2013). A new method for handling incomplete data in subspace clustering was also proposed (Fan & Chow, 2017).…”

Section: Discussionmentioning

confidence: 99%

Clustering with missing and left‐censored data: A simulation study comparing multiple‐imputation‐based procedures

et al. 2020

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Cluster analysis, commonly used to explore large biomedical datasets, can be challenging, notably due to missing data or left‐censored data induced by the sensitivity limits of the biochemical measurement method. Usually, complete‐case analysis, simple imputation, or stochastic simple imputation are applied before clustering. More recently, consensus methods following multiple imputation have been proposed. However, they ignore left‐censoring and do not allow the number of clusters to vary across the partitions of each imputed dataset. Here, we developed a consensus‐based clustering algorithm in which left‐censored data are taken into account using a modified multiple imputation method and the number of clusters is estimated for each imputed dataset. A simulation study was conducted to assess the performance in terms of the number of clusters, the percentage of unclassified observations, and the adjusted Rand index. The simulation results showed that the investigated method works well compared to several alternative approaches. A real‐world application in breast cancer patients showed that the proposed method may reveal novel clusters of patients.

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Section: Discussionmentioning

confidence: 99%

Clustering with missing and left‐censored data: A simulation study comparing multiple‐imputation‐based procedures

et al. 2020

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“…Grey relation analysis is a good method to understand the order of relevance for these conditions [14]- [17]. In addition, correlation is also a good measure to study the relevance between physical conditions and balance ability.…”

Section: Discussionmentioning

confidence: 99%

Correlation of Balance Ability and Physical Conditions

Chen¹,

Gao²,

Liao³

et al. 2017

IJPMBS

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Abstract-Generally, students with intellectual disability are not good at athletic, since they have poor balance abilities. The problem causes a bad influence for their daily life and future work. The objective of this paper is to investigate what causes this situation by measuring balance capability with Zebris FDM-System. For comparison, normal college students are also chosen as subjects for the testing. The balance test types include standing with the eyes opened and the eyes closed, and disturbing balance by rotation scheme. The indexes including BMI, leg length and footprint area are adopted as physical conditions. The T-test is used to study the significant differences between normal students and mental disability students (MDs). The results show that there are significant differences in some balance ability index between the students with mental disability and normal students. More importantly, this paper uses correlation and Grey relation to find out the relationship between balance ability and physical conditions. The results by Grey relation show BMI is the most relevant condition influencing equilibrium. However, the correlation measurement reflects that there is significant relevance just for some index.  Index Terms-intellectual disability, Zebris FDM-system, balance test, T-test, grey relation ，correlation.

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“…In the second approach, information loss may bias the results [4] and performance is worse when the missing data are not randomly distributed [5]. Broadly missing value estimation using the third approach includes listwise deletion, mean/ mode substitution, maximum likelihood, multiple imputations and machine learning based imputation [6]. In [7], authors discuss the top ten algorithms of data mining which are most promising in imputing missing value.…”

Section: Related Workmentioning

confidence: 99%

Managing uncertainty in imputing missing symptom value for healthcare of rural India

Das

Sil

2019

Health Inf Sci Syst

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In India, 67% of the total population live in remote area, where providing primary healthcare is a real challenge due to the scarcity of doctors. Health kiosks are deployed in remote villages and basic health data like blood pressure, pulse rate, height-weight, BMI, Oxygen saturation level (SpO 2) etc. are collected. The acquired data is often imprecise due to measurement error and contains missing value. The paper proposes a comprehensive framework to impute missing symptom values by managing uncertainty present in the data set. Methods: The data sets are fuzzified to manage uncertainty and fuzzy c-means clustering algorithm has been applied to group the symptom feature vectors into different disease classes. The missing symptom values corresponding to each disease are imputed using multiple fuzzy based regression model. Relations between different symptoms are framed with the help of experts and medical literature. Blood pressure symptom has been dealt with using a novel approach due to its characteristics and different from other symptoms. Patients' records obtained from the kiosks are not adequate, so relevant data are simulated by the Monte Carlo method to avoid over-fitting problem while imputing missing values of the symptoms. The generated datasets are verified using Kulberk-Leiber (K-L) distance and distance correlation (dCor) techniques, showing that the simulated data sets are well correlated with the real data set. Results: Using the data sets, the proposed model is built and new patients are provisionally diagnosed using Softmax cost function. Multiple class labels as diseases are determined by achieving about 98% accuracy and verified with the ground truth provided by the experts. Conclusions: It is worth to mention that the system is for primary healthcare and in emergency cases, patients are referred to the experts.

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Clustering‐Based Multiple Imputation via Gray Relational Analysis for Missing Data and Its Application to Aerospace Field

Cited by 11 publications

References 29 publications

Clustering with missing and left‐censored data: A simulation study comparing multiple‐imputation‐based procedures

Clustering with missing and left‐censored data: A simulation study comparing multiple‐imputation‐based procedures

Correlation of Balance Ability and Physical Conditions

Managing uncertainty in imputing missing symptom value for healthcare of rural India

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