Consensus Big Data Clustering for Bayesian Mixture Models

Karras, Christos; Karras, Aristeidis; Giotopoulos, Konstantinos C.; Avlonitis, Markos; Sioutas, Spyros

doi:10.3390/a16050245

Cited by 5 publications

(3 citation statements)

References 53 publications

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“…Furthermore, these techniques inherently capture uncertainty in the clustering process, providing a probabilistic assignment of nodes to clusters. Models such as the Dirichlet Process Mixture Model (DPMM) [36][37][38][39], Bayesian Community Detection (BCD) [40], Infinite Relational Model (IRM) [39,40], and the Chinese Restaurant Process (CRP) fall under the Bayesian nonparametric umbrella and have found applications in diverse fields, including social network analysis, bioinformatics, and community detection in complex networks [41]. Despite the unprecedented flexibility and applicability of these techniques in diverse real-world scenarios, the effective clustering of graphs remains a significant area of exploration and refinement from several perspectives.…”

Section: Literaturementioning

confidence: 99%

An Edge-Based Approach to Partitioning and Overlapping Graph Clustering with User-Specified Density

Tariq,

Lavangnananda,

Bouvry

et al. 2023

Applied Sciences

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Graph clustering has received considerable attention recently, and its applications are numerous, ranging from the detection of social communities to the clustering of computer networks. It is classified as an NP-class problem, and several algorithms have been proposed with specific objectives. There also exist various quality metrics for evaluating them. Having clusters with the required density can be beneficial because it permits the effective deployment of resources. This study proposes an approach to partitioning and overlapping clustering of undirected unweighted graphs, allowing users to specify the required density of resultant clusters. This required density is achieved by means of ‘Relative Density’. The proposed algorithm adopts an edge-based approach, commencing with the determination of the edge degree for each edge. The main clustering process is then initiated by an edge with an average degree. A cluster is expanded by considering adjacent edges that can be included while monitoring the relative density of the cluster. Eight empirical networks with diverse characteristics are used to validate the proposed algorithm for both partitioning and overlapping clustering. Their results are assessed using an appropriate metric known as the mean relative density deviation coefficient (MRDDC). This is the first work that attempts to carry out partitioning and overlapping graph clustering, which allows user-specified density.

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Section: Literaturementioning

confidence: 99%

An Edge-Based Approach to Partitioning and Overlapping Graph Clustering with User-Specified Density

Tariq,

Lavangnananda,

Bouvry

et al. 2023

Applied Sciences

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“…In addition, since Bayesian methods are methods in which the parameters are random, again the mixtures appear, giving these systems of linear inequalities an important role to model the mixtures. An example where this technique can be used is available in [39].…”

Section: Application To Artificial Intelligence Problemsmentioning

confidence: 99%

A State of the Art Review of Systems of Linear Inequalities and Related Observability Problems

Castillo

2023

Algorithms

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This work is a short review of the state of the art aiming to contribute to the use, disclosure, and propagation of systems of linear inequalities in real life, teaching, and research. It shows that the algebraic structure of their solutions consists of the sum of a linear subspace, an acute cone, and a polytope, and that adequate software exists to obtain, in their simplest forms, these three components. The work describes, based on orthogonality and polarity, homogeneous and complete systems of inequalities, the associated compatibility problems, and their relations with convex polyhedra and polytopes, which are the only possible solution for bounded problems, the most common in real practice. The compatibility and the observability problems, including their symbolic forms, are analyzed and solved, identifying the subsets of unknowns with unique solutions and those unbounded, important items of information with practical relevance in artificial intelligence and automatic learning. Having infinitely many solutions of a given problem allows us to find solutions when some of the assumptions fail and unexpected constraints come into play, a common situation for engineers. The linear programming problem becomes trivial when the set of all solutions is available and all solutions are obtained, contrary to the case of standard programs that provide only one solution. Several examples of applications to several areas of knowledge are presented, illustrating the advantages of solving these systems of inequalities.

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“…Towards the final analysis, the collection and management of large amounts of data have become an essential component in the development of technology related to AI. In particular, the fields of automated machine learning, clustering, Gibbs sampling, and data structures [110][111][112][113] have emerged in recent days due to their robustness. Particularly in managing big data on AVs, there is a vital part in the pipeline by which autonomous vehicles consume and process a vast amount and variety of data, which is essential for improving safety, security, efficiency, and the overall user experience.…”

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confidence: 99%

Autonomous Vehicles: Sophisticated Attacks, Safety Issues, Challenges, Open Topics, Blockchain, and Future Directions

Giannaros,

Karras,

Theodorakopoulos

et al. 2023

JCP

Self Cite

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Autonomous vehicles (AVs), defined as vehicles capable of navigation and decision-making independent of human intervention, represent a revolutionary advancement in transportation technology. These vehicles operate by synthesizing an array of sophisticated technologies, including sensors, cameras, GPS, radar, light imaging detection and ranging (LiDAR), and advanced computing systems. These components work in concert to accurately perceive the vehicle’s environment, ensuring the capacity to make optimal decisions in real-time. At the heart of AV functionality lies the ability to facilitate intercommunication between vehicles and with critical road infrastructure—a characteristic that, while central to their efficacy, also renders them susceptible to cyber threats. The potential infiltration of these communication channels poses a severe threat, enabling the possibility of personal information theft or the introduction of malicious software that could compromise vehicle safety. This paper offers a comprehensive exploration of the current state of AV technology, particularly examining the intersection of autonomous vehicles and emotional intelligence. We delve into an extensive analysis of recent research on safety lapses and security vulnerabilities in autonomous vehicles, placing specific emphasis on the different types of cyber attacks to which they are susceptible. We further explore the various security solutions that have been proposed and implemented to address these threats. The discussion not only provides an overview of the existing challenges but also presents a pathway toward future research directions. This includes potential advancements in the AV field, the continued refinement of safety measures, and the development of more robust, resilient security mechanisms. Ultimately, this paper seeks to contribute to a deeper understanding of the safety and security landscape of autonomous vehicles, fostering discourse on the intricate balance between technological advancement and security in this rapidly evolving field.

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Consensus Big Data Clustering for Bayesian Mixture Models

Cited by 5 publications

References 53 publications

An Edge-Based Approach to Partitioning and Overlapping Graph Clustering with User-Specified Density

An Edge-Based Approach to Partitioning and Overlapping Graph Clustering with User-Specified Density

A State of the Art Review of Systems of Linear Inequalities and Related Observability Problems

Autonomous Vehicles: Sophisticated Attacks, Safety Issues, Challenges, Open Topics, Blockchain, and Future Directions

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