The K Nearest Neighbor (kNN) method has widely been used in the applications of data
mining and machine learning due to its simple implementation and distinguished …

Our research work describes a team of human Digital Twins (DTs), each tracking fitness-
related measurements describing an athlete's behavior in consecutive days (eg food …

This paper studies an example-driven k-parameter computation that identifies different k
values for different test samples in kNN prediction applications, such as classification …

Existing kNN imputation methods for dealing with missing data are designed according to
Minkowski distance or its variants, and have been shown to be generally efficient for …

Missing data imputation is a key issue in learning from incomplete data. Various techniques
have been developed with great successes on dealing with missing values in data sets with …

Abstract Treatment of missing data has become increasingly significant in scientific research
and engineering applications. The classic imputation strategy based on the K nearest …

The presence of missing values in real-world data is not only a prevalent problem but also
an inevitable one. Therefore, missing values should be handled carefully before the mining …

This paper proposes a new k Nearest Neighbor (k NN) algorithm based on sparse learning,
so as to overcome the drawbacks of the previous k NN algorithm, such as the fixed k value …

Data preparation is an important step in mining incomplete data. To deal with this problem,
this paper introduces a new imputation approach called SN (Shell Neighbors) imputation, or …

We propose an efficient nonparametric missing value imputation method based on
clustering, called CMI (Clustering-based Missing value Imputation), for dealing with missing …