An Entropy-Based Clustering Algorithm for Real-Time High-Dimensional IoT Data Streams DOI Creative Commons
Ibrahim Mutambik

Sensors, Journal Year: 2024, Volume and Issue: 24(22), P. 7412 - 7412

Published: Nov. 20, 2024

The rapid growth of data streams, propelled by the proliferation sensors and Internet Things (IoT) devices, presents significant challenges for real-time clustering high-dimensional data. Traditional algorithms struggle with high dimensionality, memory time constraints, adapting to dynamically evolving Existing dimensionality reduction methods often neglect feature ranking, leading suboptimal performance. To address these issues, we introduce E-Stream, a novel entropy-based algorithm streams. E-Stream performs ranking based on entropy within sliding window identify most informative features, which are then utilized DenStream efficient clustering. We evaluated using NSL-KDD dataset, comparing it against DenStream, CluStream, MR-Stream. evaluation metrics included average F-Measure, Jaccard Index, Fowlkes-Mallows Purity, Rand Index. results show that outperformed baseline in both accuracy computational efficiency while effectively reducing dimensionality. also demonstrated significantly less consumption fewer requirements, highlighting its suitability processing Despite strengths, requires manual parameter adjustment assumes consistent number active may limit adaptability diverse datasets. Future work will focus developing fully autonomous, parameter-free version algorithm, incorporating mechanisms handle missing features improving management clusters enhance robustness dynamic IoT environments.

Language: Английский

Binary spectral clustering for multi-view data DOI

Xueming Yan,

Guo Zhong, Yaochu Jin

et al.

Information Sciences, Journal Year: 2024, Volume and Issue: 677, P. 120899 - 120899

Published: June 7, 2024

Language: Английский

Citations

4
An Improved Grid Clustering Algorithm for Geographic Data Mining DOI
Honglei He

Expert Systems, Journal Year: 2025, Volume and Issue: 42(5)

Published: April 1, 2025

ABSTRACT Grid clustering is a classical algorithm with the advantage of lower time complexity, which suitable for analysis large geographic data. However, it sensitive to grid division parameter M and density threshold R , accuracy poor. The article proposes hybrid HCA‐BGP based on division. first uses obtain core part class family, then division‐based method edge family. Through experiments simulated datasets real datasets, proved have better results than existing as well some other algorithms. In terms accuracy, compared Clique, F‐value this paper's improved by 20.3% dataset S1, 81.8% R15, 7.6% average eight datasets. sensitivity parameters variance clustered reduced 89.3% S1; ARI 99.9% Data8. Compared another grid‐based algorithm, GDB, also demonstrates significant advantages.

Language: Английский

Citations

0
ATSDPC: Adaptive two-stage density peaks clustering with hybrid distance based on dispersion coefficient DOI

Shengqiang Han,

Xue Zhang, Xiyu Liu

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127639 - 127639

Published: April 1, 2025

Language: Английский

Citations

0
Optimization of drilling processes in panel furniture manufacturing: A case study DOI Creative Commons
Guokun Wang, Xiaoli Li,

Xianqing Xiong

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(5), P. e0318667 - e0318667

Published: May 12, 2025

The drilling process is a crucial component in the production of panel furniture enterprises; simultaneously, it also most complex process. And enterprise’s transition to intelligent manufacturing lacks effective optimization. Therefore, this study focuses on optimizing furniture. Initially, an analysis cabinet structures was conducted, followed by data collection patterns. Based and insights from hole distribution patterns, novel COING (Coordinate Information Grid) method proposed. Subsequently, application at Company W, combined with ARM (Association Rule Mining) method, revealed inconsistencies parameters. After proposing validating solutions W’s workshop, findings demonstrated 14.0% reduction occurrences 3.87% enhancement efficiency. This demonstrates optimization processes manufacturing.

Language: Английский

Citations

0
An Entropy-Based Clustering Algorithm for Real-Time High-Dimensional IoT Data Streams DOI Creative Commons
Ibrahim Mutambik

Sensors, Journal Year: 2024, Volume and Issue: 24(22), P. 7412 - 7412

Published: Nov. 20, 2024

The rapid growth of data streams, propelled by the proliferation sensors and Internet Things (IoT) devices, presents significant challenges for real-time clustering high-dimensional data. Traditional algorithms struggle with high dimensionality, memory time constraints, adapting to dynamically evolving Existing dimensionality reduction methods often neglect feature ranking, leading suboptimal performance. To address these issues, we introduce E-Stream, a novel entropy-based algorithm streams. E-Stream performs ranking based on entropy within sliding window identify most informative features, which are then utilized DenStream efficient clustering. We evaluated using NSL-KDD dataset, comparing it against DenStream, CluStream, MR-Stream. evaluation metrics included average F-Measure, Jaccard Index, Fowlkes-Mallows Purity, Rand Index. results show that outperformed baseline in both accuracy computational efficiency while effectively reducing dimensionality. also demonstrated significantly less consumption fewer requirements, highlighting its suitability processing Despite strengths, requires manual parameter adjustment assumes consistent number active may limit adaptability diverse datasets. Future work will focus developing fully autonomous, parameter-free version algorithm, incorporating mechanisms handle missing features improving management clusters enhance robustness dynamic IoT environments.

Language: Английский

Citations

2