Random Dimension Manipulation for Efficient High-Dimensional Data Clustering

High-dimensional data is collected from various sources, fields, and applications such as medicine, science, business and more to provide helpful information to others. Unfortunately, the complexity of high-dimensional data has made it difficult to interpret and understand. As a result, sophisticate...

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Published in:Journal of Advanced Research in Applied Sciences and Engineering Technology
Main Author: Zaki U.H.H.; Kamsani I.I.; Ibrahim R.; Sakamat N.; Kandogan E.
Format: Article
Language:English
Published: Semarak Ilmu Publishing 2025
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204169136&doi=10.37934%2faraset.51.1.129140&partnerID=40&md5=794e4e2dccf85c13afc585e08b4acb08
id 2-s2.0-85204169136
spelling 2-s2.0-85204169136
Zaki U.H.H.; Kamsani I.I.; Ibrahim R.; Sakamat N.; Kandogan E.
Random Dimension Manipulation for Efficient High-Dimensional Data Clustering
2025
Journal of Advanced Research in Applied Sciences and Engineering Technology
51
1
10.37934/araset.51.1.129140
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204169136&doi=10.37934%2faraset.51.1.129140&partnerID=40&md5=794e4e2dccf85c13afc585e08b4acb08
High-dimensional data is collected from various sources, fields, and applications such as medicine, science, business and more to provide helpful information to others. Unfortunately, the complexity of high-dimensional data has made it difficult to interpret and understand. As a result, sophisticated data analysis is required to extract knowledge and information from it. This can be illustrated through a visualization presentation. However, overlap between data can occur during visualization as data increases. Indirectly, it can cause a cluttered visual presentation. As a result, it affects the visual perception of high-dimensional data patterns. High-dimensional data can be deeply explored using dimension arrangement and scaling to overcome it. The arrangement of dimensions is essential since the relationship between these dimensions can influence the existence of an efficient cluster. This dimension is arranged based on the correlation value. The dimension that is more related will be placed next to each other. While performing clusters, dimensions will be scaled in or out. These features are available through Star Coordinate (SC) technique. This paper aims to conduct an exploratory data analysis in the SC environment where users can visualize and interact in a low-dimensional data visualization space. This paper demonstrates data dimensions manipulation's importance in structuring the projected space layout using two data sets. As a conclusion, formation of clusters was crucial and manipulation of data dimensions were essential to structure the projected space layout. The proposed approach has helped users find significant cluster formations by randomizing the scaling and order of dimensions. © 2025, Semarak Ilmu Publishing. All rights reserved.
Semarak Ilmu Publishing
24621943
English
Article

author Zaki U.H.H.; Kamsani I.I.; Ibrahim R.; Sakamat N.; Kandogan E.
spellingShingle Zaki U.H.H.; Kamsani I.I.; Ibrahim R.; Sakamat N.; Kandogan E.
Random Dimension Manipulation for Efficient High-Dimensional Data Clustering
author_facet Zaki U.H.H.; Kamsani I.I.; Ibrahim R.; Sakamat N.; Kandogan E.
author_sort Zaki U.H.H.; Kamsani I.I.; Ibrahim R.; Sakamat N.; Kandogan E.
title Random Dimension Manipulation for Efficient High-Dimensional Data Clustering
title_short Random Dimension Manipulation for Efficient High-Dimensional Data Clustering
title_full Random Dimension Manipulation for Efficient High-Dimensional Data Clustering
title_fullStr Random Dimension Manipulation for Efficient High-Dimensional Data Clustering
title_full_unstemmed Random Dimension Manipulation for Efficient High-Dimensional Data Clustering
title_sort Random Dimension Manipulation for Efficient High-Dimensional Data Clustering
publishDate 2025
container_title Journal of Advanced Research in Applied Sciences and Engineering Technology
container_volume 51
container_issue 1
doi_str_mv 10.37934/araset.51.1.129140
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204169136&doi=10.37934%2faraset.51.1.129140&partnerID=40&md5=794e4e2dccf85c13afc585e08b4acb08
description High-dimensional data is collected from various sources, fields, and applications such as medicine, science, business and more to provide helpful information to others. Unfortunately, the complexity of high-dimensional data has made it difficult to interpret and understand. As a result, sophisticated data analysis is required to extract knowledge and information from it. This can be illustrated through a visualization presentation. However, overlap between data can occur during visualization as data increases. Indirectly, it can cause a cluttered visual presentation. As a result, it affects the visual perception of high-dimensional data patterns. High-dimensional data can be deeply explored using dimension arrangement and scaling to overcome it. The arrangement of dimensions is essential since the relationship between these dimensions can influence the existence of an efficient cluster. This dimension is arranged based on the correlation value. The dimension that is more related will be placed next to each other. While performing clusters, dimensions will be scaled in or out. These features are available through Star Coordinate (SC) technique. This paper aims to conduct an exploratory data analysis in the SC environment where users can visualize and interact in a low-dimensional data visualization space. This paper demonstrates data dimensions manipulation's importance in structuring the projected space layout using two data sets. As a conclusion, formation of clusters was crucial and manipulation of data dimensions were essential to structure the projected space layout. The proposed approach has helped users find significant cluster formations by randomizing the scaling and order of dimensions. © 2025, Semarak Ilmu Publishing. All rights reserved.
publisher Semarak Ilmu Publishing
issn 24621943
language English
format Article
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