Performance Analysis of Hard Clustering Techniques for Big IoT Data Analytics

Ahmed, Mohiuddin; Barkat, Abu

doi:10.1109/ccc.2019.000-8

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…Clustering is a type of unsupervised learning method based on the grouping of data with similar characteristics and is classified as soft clustering [10], hard clustering [13], and hierarchical clustering [15,16] (i.e., the clustering of data based on the probability of belonging to a cluster, depending on the distance between clusters). Table 2 presents an analysis of the related clustering studies.…”

Section: B Clusteringmentioning

confidence: 99%

Performance Improvement of Fuzzy C-Means Clustering Algorithm by Optimized Early Stopping for Inhomogeneous Datasets

Han,

Lee,

Lee

2023

JICCE

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Responding to changes in artificial intelligence models and the data environment is crucial for increasing data-learning accuracy and inference stability of industrial applications. A learning model that is overfitted to specific training data leads to poor learning performance and a deterioration in flexibility. Therefore, an early stopping technique is used to stop learning at an appropriate time. However, this technique does not consider the homogeneity and independence of the data collected by heterogeneous nodes in a differential network environment, thus resulting in low learning accuracy and degradation of system performance. In this study, the generalization performance of neural networks is maximized, whereas the effect of the homogeneity of datasets is minimized by achieving an accuracy of 99.7%. This corresponds to a decrease in delay time by a factor of 2.33 and improvement in performance by a factor of 2.5 compared with the conventional method.

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Section: B Clusteringmentioning

confidence: 99%

Performance Improvement of Fuzzy C-Means Clustering Algorithm by Optimized Early Stopping for Inhomogeneous Datasets

Han,

Lee,

Lee

2023

JICCE

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“…Some well-known forms of regression methods include linear, polynomial, lasso, and ridge regression, among others [46]. Cluster analysis, alternatively referred to as clustering, is an unsupervised ML method used to identify and group similar data points in large datasets without concern for the particular outcome [47]. Clustering can be used in various fields of use, such as cybersecurity, electronic commerce, mobile data processing, health analysis, and user modeling [48].…”

Section: Entropy= -Log2 P( XI )mentioning

confidence: 99%

A Review on Cybersecurity based on Machine Learning and Deep Learning Algorithms

Jahwar

Ameen

2021

JSCDM

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Machin learning (ML) and Deep Learning (DL) technique have been widely applied to areas like image processing and speech recognition so far. Likewise, ML and DL play a critical role in detecting and preventing in the field of cybersecurity. In this review, we focus on recent ML and DL algorithms that have been proposed in cybersecurity, network intrusion detection, malware detection. We also discuss key elements of cybersecurity, the main principle of information security, and the most common methods used to threaten cybersecurity. Finally, concluding remarks are discussed, including the possible research topics that can be taken into consideration to enhance various cyber security applications using DL and ML algorithms.

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RansomCoin: A New Dataset for Analysing Cryptocurrency Transactions - Addressing a Gap in the Literature

Ahmed,

Pagutaisidro,

Alexander Pike

et al. 2023

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

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Performance Analysis of Hard Clustering Techniques for Big IoT Data Analytics

Cited by 5 publications

References 13 publications

Performance Improvement of Fuzzy C-Means Clustering Algorithm by Optimized Early Stopping for Inhomogeneous Datasets

Performance Improvement of Fuzzy C-Means Clustering Algorithm by Optimized Early Stopping for Inhomogeneous Datasets

A Review on Cybersecurity based on Machine Learning and Deep Learning Algorithms

RansomCoin: A New Dataset for Analysing Cryptocurrency Transactions - Addressing a Gap in the Literature

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