Open Access

ARTICLE

Deep Learning and Tensor-Based Multiple Clustering Approaches for Cyber-Physical-Social Applications

Hongjun Zhang^1,2, Hao Zhang², Yu Lei³, Hao Ye¹, Peng Li^1,*, Desheng Shi¹

1 School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
2 China Communications Services Co., Ltd., Beijing, 100071, China
3 China Communications Services Hexin Science & Technology Co., Ltd., Hefei, 230031, China

* Corresponding Author: Peng Li. Email:

Computers, Materials & Continua 2024, 78(3), 4109-4128. https://doi.org/10.32604/cmc.2024.048355

Received 29 November 2023; Accepted 29 December 2023; Issue published 26 March 2024

Abstract

The study delves into the expanding role of network platforms in our daily lives, encompassing various mediums like blogs, forums, online chats, and prominent social media platforms such as Facebook, Twitter, and Instagram. While these platforms offer avenues for self-expression and community support, they concurrently harbor negative impacts, fostering antisocial behaviors like phishing, impersonation, hate speech, cyberbullying, cyberstalking, cyberterrorism, fake news propagation, spamming, and fraud. Notably, individuals also leverage these platforms to connect with authorities and seek aid during disasters. The overarching objective of this research is to address the dual nature of network platforms by proposing innovative methodologies aimed at enhancing their positive aspects and mitigating their negative repercussions. To achieve this, the study introduces a weight learning method grounded in multi-linear attribute ranking. This approach serves to evaluate the significance of attribute combinations across all feature spaces. Additionally, a novel clustering method based on tensors is proposed to elevate the quality of clustering while effectively distinguishing selected features. The methodology incorporates a weighted average similarity matrix and optionally integrates weighted Euclidean distance, contributing to a more nuanced understanding of attribute importance. The analysis of the proposed methods yields significant findings. The weight learning method proves instrumental in discerning the importance of attribute combinations, shedding light on key aspects within feature spaces. Simultaneously, the clustering method based on tensors exhibits improved efficacy in enhancing clustering quality and feature distinction. This not only advances our understanding of attribute importance but also paves the way for more nuanced data analysis methodologies. In conclusion, this research underscores the pivotal role of network platforms in contemporary society, emphasizing their potential for both positive contributions and adverse consequences. The proposed methodologies offer novel approaches to address these dualities, providing a foundation for future research and practical applications. Ultimately, this study contributes to the ongoing discourse on optimizing the utility of network platforms while minimizing their negative impacts.

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Keywords

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