<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec>
<h3>Purpose</h3>
<p>Exploring a dimensionality reduction model that can adeptly eliminate outliers and select the appropriate number of clusters is of profound theoretical and practical importance. Additionally, the interpretability of these models presents a persistent challenge.</p>
</sec>
<sec>
<h3>Design/methodology/approach</h3>
<p>This paper proposes two innovative dimensionality reduction models based on integer programming (DRMBIP). These models assess compactness through the correlation of each indicator with its class center, while separation is evaluated by the correlation between different class centers. In contrast to DRMBIP-p, the DRMBIP-v considers the threshold parameter as a variable aiming to optimally balances both compactness and separation.</p>
</sec>
<sec>
<h3>Findings</h3>
<p>This study, getting data from the Global Health Observatory (GHO), investigates 141 indicators that influence life expectancy. The findings reveal that DRMBIP-p effectively reduces the dimensionality of data, ensuring compactness. It also maintains compatibility with other models. Additionally, DRMBIP-v finds the optimal result, showing exceptional separation. Visualization of the results reveals that all classes have a high compactness.</p>
</sec>
<sec>
<h3>Research limitations</h3>
<p>The DRMBIP-p requires the input of the correlation threshold parameter, which plays a pivotal role in the effectiveness of the final dimensionality reduction results. In the DRMBIP-v, modifying the threshold parameter to variable potentially emphasizes either separation or compactness. This necessitates an artificial adjustment to the overflow component within the objective function.</p>
</sec>
<sec>
<h3>Practical implications</h3>
<p>The DRMBIP presented in this paper is adept at uncovering the primary geometric structures within high-dimensional indicators. Validated by life expectancy data, this paper demonstrates potential to assist data miners with the reduction of data dimensions.</p>
</sec>
<sec>
<h3>Originality/value</h3>
<p>To our knowledge, this is the first time that integer programming has been used to build a dimensionality reduction model with indicator filtering. It not only has applications in life expectancy, but also has obvious advantages in data mining work that requires precise class centers.</p>
</sec>
</abstract>

Purpose
Exploring a dimensionality reduction model that can adeptly eliminate outliers and select the appropriate number of clusters is of profound theoretical and practical importance. Additionally, the interpretability of these models presents a persistent challenge.


Design/methodology/approach
This paper proposes two innovative dimensionality reduction models based on integer programming (DRMBIP). These models assess compactness through the correlation of each indicator with its class center, while separation is evaluated by the correlation between different class centers. In contrast to DRMBIP-p, the DRMBIP-v considers the threshold parameter as a variable aiming to optimally balances both compactness and separation.


Findings
This study, getting data from the Global Health Observatory (GHO), investigates 141 indicators that influence life expectancy. The findings reveal that DRMBIP-p effectively reduces the dimensionality of data, ensuring compactness. It also maintains compatibility with other models. Additionally, DRMBIP-v finds the optimal result, showing exceptional separation. Visualization of the results reveals that all classes have a high compactness.


Research limitations
The DRMBIP-p requires the input of the correlation threshold parameter, which plays a pivotal role in the effectiveness of the final dimensionality reduction results. In the DRMBIP-v, modifying the threshold parameter to variable potentially emphasizes either separation or compactness. This necessitates an artificial adjustment to the overflow component within the objective function.


Practical implications
The DRMBIP presented in this paper is adept at uncovering the primary geometric structures within high-dimensional indicators. Validated by life expectancy data, this paper demonstrates potential to assist data miners with the reduction of data dimensions.


Originality/value
To our knowledge, this is the first time that integer programming has been used to build a dimensionality reduction model with indicator filtering. It not only has applications in life expectancy, but also has obvious advantages in data mining work that requires precise class centers.

Dimensionality reduction model based on integer planning for the analysis of key indicators affecting life expectancy

Journal of Data and Information Science

This work is licensed under the Creative Commons Attribution 4.0 International License.

Purpose
Exploring a dimensionality reduction model that can adeptly eliminate outliers and select the appropriate number of clusters is of profound theoretical...

Dimensionality reduction model based on integer planning for the analysis of key indicators affecting life expectancy

Article Category: Research Paper

Online veröffentlicht: 30. Nov. 2023

Seitenbereich: 102 - 124

Eingereicht: 19. Okt. 2023

Akzeptiert: 09. Nov. 2023

DOI: https://doi.org/10.2478/jdis-2023-0025

Schlüsselwörter
Integer programming, Multidimensional data, Dimensionality reduction, Life expectancy

© 2023 Wei Cui et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Dimensionality reduction model based on integer planning for the analysis of key indicators affecting life expectancy

Article Category: Research Paper

Online veröffentlicht: 30. Nov. 2023

Seitenbereich: 102 - 124

Eingereicht: 19. Okt. 2023

Akzeptiert: 09. Nov. 2023

DOI: https://doi.org/10.2478/jdis-2023-0025

SchlüsselwörterInteger programming, Multidimensional data, Dimensionality reduction, Life expectancy

© 2023 Wei Cui et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Integer programming, Multidimensional data, Dimensionality reduction, Life expectancy