Transfer Clustering Based on Gaussian Mixture Model

Wang, Rongrong; Zhou, Jin; Liu, Xiangdao; Han, Shiyuan; Wang, Lin; Chen, Yuehui

doi:10.1109/ssci44817.2019.9003043

Cited by 8 publications

(1 citation statement)

References 23 publications

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“…Gaussian mixture models employed for clustering suppose that every data point forms a Gaussian distribution. This clustering technique is believed to outperform other clustering algorithms as it factors in the number of clusters and the position of the same along with the shape [ 45 , 46 , 47 ].…”

Section: System Modelmentioning

confidence: 99%

Towards a Machine Learning Driven Trust Management Heuristic for the Internet of Vehicles

Siddiqui

Mahmood

Sheng

et al. 2023

Sensors

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The rapid proliferation of the emerging yet promising notion of the Internet-of-Vehicles (IoV) has led to the development of a variety of conventional trust assessment schemes to tackle insider attackers. The primary reliance of these frameworks is on the accumulation of individual trust attributes. While aggregating these influential parameters, weights are often associated with each individual attribute to reflect its impact on the final trust score. It is of paramount importance that such weights be precise to lead to an accurate trust assessment. Moreover, the value of the minimum acceptable trust threshold employed for the identification of dishonest vehicles needs to be carefully defined to avoid delayed or erroneous detection. This paper employs an IoT data set from CRAWDAD by suitably transforming it into an IoV format. This data set encompasses information regarding 18,226 interactions among 76 nodes, both honest and dishonest. First, the influencing parameters (i.e., packet delivery ratio, familiarity, timeliness and interaction frequency) were computed, and two feature matrices were formed. The first matrix (FM1) takes into account all the pairwise individual parameters as individual features, whereas the second matrix (FM2) considers the average of all pairwise computations performed for each individual parameter as one feature. Subsequently, unsupervised learning is employed to achieve the ground truth prior to applying supervised machine learning algorithms for classification purposes. It is worth noting that Subspace KNN yielded a perfect precision, recall, and the F1-score equal to 1 for individual parametric scores, whereas Subspace Discriminant returned an ideal precision, recall, and the F1-score equal to 1 for mean parametric scores. It is also evident from extensive simulations that FM2 yielded more accurate classification results compared to FM1. Furthermore, decision boundaries among honest and dishonest vehicles have also been computed for respective feature matrices.

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Section: System Modelmentioning

confidence: 99%