Selection of Optimal Number of Clusters and Centroids for K-means and Fuzzy C-means Clustering: A Review

A, Pugazhenthi; Kumar, Lakshmi Sutha

doi:10.1109/icccs49678.2020.9276978

Cited by 15 publications

(3 citation statements)

References 21 publications

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“…To ensure the reliability of the proposed model, crossvalidation was implemented, and the optimal number of folds was determined using the K-fold method [25] [26]. By selecting the most suitable value of K through K-fold cross-validation, overfitting was reduced, and the accuracy of the model was improved.…”

Section: Discussionmentioning

confidence: 99%

Hybrid Machine Learning-Based Approach for Anomaly Detection using Apache Spark

Chliah¹,

Battou²,

hadj³

et al. 2023

IJACSA

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Over the past few decades, the volume of data has increased significantly in both scientific institutions and universities, with a large number of students enrolled and a high volume of related data. Furthermore, network traffic has increased with post-pandemic and the use of online learning. Therefore, processing network traffic data is a complex and challenging task that increases the possibility of intrusions and anomalies. Traditional security systems cannot deal with such high-speed and big data traffic. Real-time anomaly detection should be able to process data as quickly as possible to detect abnormal and malicious data. This paper proposes a hybrid approach consisting of supervised and unsupervised learning for anomaly detection based on the big data engine Apache Spark. Initially, the k-means algorithm was implemented in Sparks MLlib for clustering network traffic, then for each cluster, K-nearest neighbors algorithm (KN N ) was implemented for classification and anomaly detection. The proposed model was trained and validated against a real dataset from Ibn Zohr University. The results indicate that the proposed model outperformed other well-known algorithms in detecting anomalies based on the aforementioned dataset. The experimental results show that the proposed hybrid approach can reach up to 99.94 % accuracy using the k-fold cross-validation method in the complete dataset with all 48 features.

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Section: Discussionmentioning

confidence: 99%

Hybrid Machine Learning-Based Approach for Anomaly Detection using Apache Spark

Chliah¹,

Battou²,

hadj³

et al. 2023

IJACSA

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“…K-means algorithms are recognized as the most well-known and earliest partitional method [ 37 ]. Initialization parameters influence these algorithms in addition to assigning in prior to these clusters [ 38 , 39 , 40 ]. K-means clustering converges fast due to its simplicity [ 41 ].…”

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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“…The procedure of regeneration is repeated for a particular number of generations or until no more improvement is observed. The best chromosome produced in last generation of the optimal resulted chromosome, which representing the solution for the problem of GA optimization [16]. Such that, the last generation shows final nodes locations and specific number of CH in the considered area of WSN [10].…”

Section: Ga For Wsn Optimizationmentioning

confidence: 99%

K-Means clustering of optimized wireless network sensor using genetic algorithm

Kadim¹,

Al-Mukhtar²,

Hasan³

et al. 2022

PEN

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Wireless sensor network is one of the main technology trends that used in several different applications for collecting, processing, and distributing a vast range of data. It becomes an essential core technology for many applications related to sense surrounding environment. In this paper, a two-dimensional WSN scheme was utilized for obtaining various WSN models that intended to be optimized by genetic algorithm for achieving optimized WSN models. Such optimized WSN models might contain two cluster heads that are close to each other, in which the distance between them included in the sensing range, and this demonstrates the presence of a redundant number of cluster heads. This problem exceeded by reapplying the clustering of all sensors found in the WSN model. The distance measure was used to detect handled problem, while K-means clustering was used to redistributing sensors around the alternative cluster head. The result was extremely encouraging in rearranging the dispersion of sensors in the detecting region with a conservative method of modest number of cluster heads that acknowledge the association for all sensors nearby.

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Selection of Optimal Number of Clusters and Centroids for K-means and Fuzzy C-means Clustering: A Review

Cited by 15 publications

References 21 publications

Hybrid Machine Learning-Based Approach for Anomaly Detection using Apache Spark

Hybrid Machine Learning-Based Approach for Anomaly Detection using Apache Spark

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

K-Means clustering of optimized wireless network sensor using genetic algorithm

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