Missing Value Imputation Based on K-Mean Clustering with Weighted Distance

Patil, B. M.; Joshi, R. C.; Toshniwal, Durga

doi:10.1007/978-3-642-14834-7_56

Cited by 30 publications

(17 citation statements)

References 10 publications

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“…A number of methods based on k-means clustering algorithm have been proposed to solve the problem of missing data imputation (Patil, Joshi & Toshniwal, 2010;Jiang & Yang, 2015). Patil, Joshi & Toshniwal (2010) proposed an efficient missing value imputation method based on k-means clustering with weighted distance. They use the user-specified value k to divide the dataset into clusters.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, k value is determined by the user so determining the appropriate k value can be a challenging task (Batista & Monard, 2003;Rahman & Islam, 2013b;Liu et al, 2015). Recently, a number of methods based on k-means clustering algorithm have been proposed to solve the problem of missing data imputation (Patil, Joshi & Toshniwal, 2010;Jiang & Yang, 2015). The basic idea behind these techniques is to estimate a missing value in a record based on the cluster information in which the missing record is located.…”

Section: Introductionmentioning

confidence: 99%

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Advanced methods for missing values imputation based on similarity learning

Fouad

Ismail

Azar

et al. 2021

PeerJ Computer Science

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The real-world data analysis and processing using data mining techniques often are facing observations that contain missing values. The main challenge of mining datasets is the existence of missing values. The missing values in a dataset should be imputed using the imputation method to improve the data mining methods’ accuracy and performance. There are existing techniques that use k-nearest neighbors algorithm for imputing the missing values but determining the appropriate k value can be a challenging task. There are other existing imputation techniques that are based on hard clustering algorithms. When records are not well-separated, as in the case of missing data, hard clustering provides a poor description tool in many cases. In general, the imputation depending on similar records is more accurate than the imputation depending on the entire dataset's records. Improving the similarity among records can result in improving the imputation performance. This paper proposes two numerical missing data imputation methods. A hybrid missing data imputation method is initially proposed, called KI, that incorporates k-nearest neighbors and iterative imputation algorithms. The best set of nearest neighbors for each missing record is discovered through the records similarity by using the k-nearest neighbors algorithm (kNN). To improve the similarity, a suitable k value is estimated automatically for the kNN. The iterative imputation method is then used to impute the missing values of the incomplete records by using the global correlation structure among the selected records. An enhanced hybrid missing data imputation method is then proposed, called FCKI, which is an extension of KI. It integrates fuzzy c-means, k-nearest neighbors, and iterative imputation algorithms to impute the missing data in a dataset. The fuzzy c-means algorithm is selected because the records can belong to multiple clusters at the same time. This can lead to further improvement for similarity. FCKI searches a cluster, instead of the whole dataset, to find the best k-nearest neighbors. It applies two levels of similarity to achieve a higher imputation accuracy. The performance of the proposed imputation techniques is assessed by using fifteen datasets with variant missing ratios for three types of missing data; MCAR, MAR, MNAR. These different missing data types are generated in this work. The datasets with different sizes are used in this paper to validate the model. Therefore, proposed imputation techniques are compared with other missing data imputation methods by means of three measures; the root mean square error (RMSE), the normalized root mean square error (NRMSE), and the mean absolute error (MAE). The results show that the proposed methods achieve better imputation accuracy and require significantly less time than other missing data imputation methods.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advanced methods for missing values imputation based on similarity learning

Fouad

Ismail

Azar

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…Typical clustering methods, such as k-means, hierarchical clustering [29] and k-means clustering with weighted distance [30] have been generally employed to improve the imputation performance in incomplete datasets. However, clustering methods are not robust enough to missing data [26].…”

Section: C: Clustering Imputationmentioning

confidence: 99%

Iterative Robust Semi-Supervised Missing Data Imputation

et al. 2020

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In many real-world applications scientists are often confronted with the problem of incomplete datasets due to several reasons. The direct analysis of datasets with missing values in attributes inevitably results in inaccurate learning models and erroneous results. Facing effectively the challenge of missing values is an essential step of the data mining process. Imputation is often employed to overcome the shortcomings incurred by missing data during the pre-process stage of data analysis. Therefore, a plethora of statistical and machine learning methods have been proposed and employed with a view to imputing the missing values in incomplete data with their potential or actual values. In this context, the main objective of this paper is to put forward an iterative stepwise imputation method based on the semi-supervised learning approach, called IRSSI. Semi-supervised methods have proved to be particularly effective for exploiting incomplete or partially labeled data with regard to the values of the target attribute. The proposed algorithm was experimentally evaluated on real-world benchmark datasets and artificially generated datasets using different high ratios of missing data. The experimental results demonstrate the efficiency of IRSSI algorithm compared to typical imputation methods.

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“…Statistical methods were also explored in the medical domain [33]. Many researchers have used K-means clustering algorithm (KMCA) to impute missing values in medical data [34] and financial data [35]. K-means clustering algorithm takes input parameter k (number of clusters) and partition data into k clusters with high inter-cluster similarity based on distance function.…”

Section: K-means Clusteringmentioning

confidence: 99%

An intelligent noninvasive model for coronary artery disease detection

Verma

Srivastava

Negi

2017

Complex Intell. Syst.

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Coronary artery disease (CAD) is one of the leading causes of death globally. Angiography is one of the benchmarked diagnoses for detection of CAD; however, it is costly, invasive, and requires a high level of technical expertise. This paper discusses a data mining technique that uses noninvasive clinical data to identify CAD cases. The clinical data of 335 subjects were collected at the cardiology department, Indira Gandhi Medical College, Shimla, India, over the period of 2012-2013. Only 48.9% subjects showed coronary stenosis in coronary angiography and were confirmed cases of CAD. A large number of cases (171 out of 335) were found normal after invasive diagnosis. Hence, a requirement of noninvasive technique was felt that could identify CAD cases without going for invasive diagnosis. We applied data mining classification techniques on noninvasive clinical data. The data set is analyzed using a hybrid and novel k-means cluster centroid-based method for missing value imputation and C4.5, NB Tree and multilayer perceptron for modeling to predict CAD patients. The proposed hybrid method increases the accuracy achieved by the basic techniques of classification. This framework is a promising tool for screening CAD and its severity with high probability and low cost.

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Missing Value Imputation Based on K-Mean Clustering with Weighted Distance

Cited by 30 publications

References 10 publications

Advanced methods for missing values imputation based on similarity learning

Advanced methods for missing values imputation based on similarity learning

Iterative Robust Semi-Supervised Missing Data Imputation

An intelligent noninvasive model for coronary artery disease detection

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