Research and Optimization of Distributed Clustering Algorithms for High-Dimensional Data

Runfeng Zou

doi:10.47297/taposatWSP2633-456935.20250603

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Research and Optimization of Distributed Clustering Algorithms for High-Dimensional Data

6卷第3期

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Research and Optimization of Distributed Clustering Algorithms for High-Dimensional Data
Research and Optimization of Distributed Clustering Algorithms for High-Dimensional Data
Queen's University of Kingston, Canada
2025年6卷第3期页码：188-195
纸质出版日期：2025-03-25
DOI：10.47297/taposatWSP2633-456935.20250603
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(2025). Research and Optimization of Distributed Clustering Algorithms for High-Dimensional Data. 科技理论与实践（英文版）, 6(3), 188-195.

Zou Runfeng.Research and Optimization of Distributed Clustering Algorithms for High-Dimensional Data[J].Theory and Practice of Science and Technology,2025,06(03):188-195.
(2025). Research and Optimization of Distributed Clustering Algorithms for High-Dimensional Data. 科技理论与实践（英文版）, 6(3), 188-195. DOI： 10.47297/taposatWSP2633-456935.20250603.

Zou Runfeng.Research and Optimization of Distributed Clustering Algorithms for High-Dimensional Data[J].Theory and Practice of Science and Technology,2025,06(03):188-195. DOI： 10.47297/taposatWSP2633-456935.20250603.

摘要

Abstract

High-dimensional data analysis is a crucial task in various domains

including bioinformatics

image recognition

and text mining. However

traditional clustering algorithms

such as K-means and DBSCAN

struggle with the "curse of dimensionality

" leading to inefficiencies in both accuracy and computational performance. To address these challenges

this paper proposes an optimized approach that integrates dimensionality reduction techniques with clustering algorithms in a distributed computing environment. Specifically

we evaluate and compare the effectiveness of principal component analysis (PCA)

t-distributed stochastic neighbor embedding (t-SNE)

and autoencoders for dimensionality reduction

followed by K-means and DBSCAN clustering. Experiments are conducted on high-dimensional datasets

including text

image

and genomic data

to assess clustering quality

computational efficiency

and scalability. The results demonstrate that combining appropriate dimensionality reduction methods with clustering significantly improves performance while mitigating the impact of high dimensionality. Furthermore

our distributed computing framework enhances scalability

making it feasible for large-scale data processing. This study provides a comprehensive evaluation of different combinations of dimensionality reduction and clustering methods

offering practical insights into optimizing high-dimensional data clustering for real-world applications.

关键词

Keywords

references

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