Enhancement of OPTICS’ time complexity by using fuzzy clusters

Kamil, Israa S.; Al‐Mamory, Safaa O.

doi:10.1016/j.matpr.2021.06.441

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…After the order of points is obtained, groups of data are defined as such. In this study, to improve the OPTICS performance, the version presented in the study by Kamil and Al-Mamory ( 2023 ) is considered. Specifically, it comprises two phases.…”

Section: Clustering Algorithms Selected For This Investigationmentioning

confidence: 99%

A comprehensive investigation of clustering algorithms for User and Entity Behavior Analytics

Artioli,

Maci,

Magrì

2024

Front. Big Data

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IntroductionGovernment agencies are now encouraging industries to enhance their security systems to detect and respond proactively to cybersecurity incidents. Consequently, equipping with a security operation center that combines the analytical capabilities of human experts with systems based on Machine Learning (ML) plays a critical role. In this setting, Security Information and Event Management (SIEM) platforms can effectively handle network-related events to trigger cybersecurity alerts. Furthermore, a SIEM may include a User and Entity Behavior Analytics (UEBA) engine that examines the behavior of both users and devices, or entities, within a corporate network.MethodsIn recent literature, several contributions have employed ML algorithms for UEBA, especially those based on the unsupervised learning paradigm, because anomalous behaviors are usually not known in advance. However, to shorten the gap between research advances and practice, it is necessary to comprehensively analyze the effectiveness of these methodologies. This paper proposes a thorough investigation of traditional and emerging clustering algorithms for UEBA, considering multiple application contexts, i.e., different user-entity interaction scenarios.Results and discussionOur study involves three datasets sourced from the existing literature and fifteen clustering algorithms. Among the compared techniques, HDBSCAN and DenMune showed promising performance on the state-of-the-art CERT behavior-related dataset, producing groups with a density very close to the number of users.

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Section: Clustering Algorithms Selected For This Investigationmentioning

confidence: 99%

A comprehensive investigation of clustering algorithms for User and Entity Behavior Analytics

Artioli,

Maci,

Magrì

2024

Front. Big Data

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“…This paper validates the AP performance by a comprehensive comparison with nine other prominent unsupervised clustering algorithms' performance. These clustering algorithms include distance-based methods such as KM [20] and MBKM [21], density-based techniques such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN) [22] and Ordering Points to Identify the Clustering Structure (OPTICS) [23], and hierarchical methods such as Agglomerative Hierarchical Clustering (AHC) [24] and Balanced Iterative Reducing and Clustering using Hierarchies (BRICH) [25]. Additionally, model-based Gaussian Mixture Models (GMM) algorithms [26], [27], kernel-based Mean Shift (MS) [28], and SC [29] are evaluated.…”

Section: Affinity Propagation Clustering Modelmentioning

confidence: 99%

Optimum Partition of Power Networks Using Singular Value Decomposition and Affinity Propagation

Ez Eddin,

Massaoudi,

Abu-Rub

et al. 2024

IEEE Trans. Power Syst.

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“…There are many works in the field of data clustering in the scientific field, and a significant part of them deal with the development of the algorithms themselves, not to their application, for example, the work of researchers [11].The studies in the banking field often focus on a particular side of the bank, the business line, for example author [12] examined the bank's credit risk. The subject of a comprehensive analysis of the banking system using methods and tools of unsupervised machine learning with data dimensionality reduction algorithms has not been widely covered in the scientific literature, while it is even more difficult to find and qualitative research in the context of Ukrainian realities.…”

Section: Introductionmentioning

confidence: 99%

Application of Clustering in the Dimensionality Reduction Algorithms for Separation of Financial Status of Commercial Banks in Ukraine

Velykoivanenko¹,

Korchynskyi²

2022

ujaf

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The issue of determining the financial condition of commercial banks and separating investment-attractive banks from problem banks on this ground is extremely important for developing countries. The aim of this study is to make sure on the example of Ukraine that commercial banks really form separate clusters, where more reliable, stable and efficient banks are well separable from less successful ones in this regard. The study used the t-SNE and UMAP dimensionality reduction algorithms, and the Ward's Agglomerative Hierarchical Clustering algorithm. The results of visual analysis of two-dimensional t-SNE projections show that banks of different degrees of risk are well separable and have their own specifics. Clustering in the UMAP algorithm allowed distinguishing clusters with banks of Class A, "mid-tier" and problematic banks by different parameters. The t-SNE and UMAP algorithms for solving the problem are compared. The results show that a purely visual analysis of the two-dimensional map for the banks over the last period is best made using the t-SNE algorithm. UMAP, on the other hand, is proved to be excellent when used in tandem with the clustering algorithm.

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Enhancement of OPTICS’ time complexity by using fuzzy clusters

Cited by 6 publications

References 14 publications

A comprehensive investigation of clustering algorithms for User and Entity Behavior Analytics

A comprehensive investigation of clustering algorithms for User and Entity Behavior Analytics

Optimum Partition of Power Networks Using Singular Value Decomposition and Affinity Propagation

Application of Clustering in the Dimensionality Reduction Algorithms for Separation of Financial Status of Commercial Banks in Ukraine

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