In the era of big data, the continuous generation of data from various fields has resulted in large and complex datasets. These datasets often come in diverse formats and structures, including unstructured or semi-structured data. Despite the wide availability of big data, high dimensionality remains a significant challenge for analysing and understanding the data for various purposes. Clustering analysis plays a crucial role in data analysis and visualization by uncovering hidden patterns and structures within datasets. However, several challenges hinder the effectiveness of clustering analysis, including data dimensionality, selection of appropriate clustering algorithms, determining the optimal number of clusters, interpreting the results, and handling outliers. This paper aims to explore these challenges and presents preferable visualization techniques that aid in visualizing and interpreting clustering results. By addressing these challenges, including the difficulty of handling outliers and the struggles with high-dimensional datasets, and employing effective visualization techniques, researchers and practitioners can enhance their understanding and utilization of clustering analysis in data analysis.

تستخدم معظم الشركات بيانات وسائل التواصل الاجتماعي للأعمال. يقوم تحليل المشاعر تلقائيًا بجمع التحليلات وتلخيص هذا النوع من البيانات. من الصعب إدارة بيانات وسائل التواصل الاجتماعي غير المنظمة. حيث تمثل البيانات المزعجة او غير متناسقة تحديًا لتحليل المشاعر. نظرًا لأن أكثر من 50٪ من عملية تحليل المشاعر هي معالجة مسبقة للبيانات، فإن معالجة بيانات الوسائط الاجتماعية الضخمة تمثل تحديًا أيضًا. إذا تم إجراء المعالجة المسبقة بشكل صحيح، فقد تتحسن دقة البيانات. أيضًا، يعتمد سير العمل في تحليل المشاعر بشكل كبير. نظرًا لعدم وجود تقنية معالجة مسبقة تعمل بشكل جيد في جميع المواقف أو مع جميع مصادر البيانات، فإن اختيار أهم المصادر أمر بالغ الأهمية. تحديد الأولويات هو أسلوب ممتاز لاختيار الأكثر أهمية.باعتبارها إحدى طرق اتخاذ القرار متعدد المعايير (MCDM)، تُفضل عملية التحليل الهرمي (AHP) التعامل مع تحديات صنع القرار المعقدة باستخدام عدة معايير. تم استخدام درجات نسبة الاتساق (CR) لفحص المقارنات الزوجية لتقييم AHP. استخدمت هذه الدراسة مقياسين للحصول على الحكم الأكثر اتساقًا. أولاً، مقياس حكم Saaty (SS)، ثم المقياس المتوازن المعمم (GBS). لقد تم...

High-dimensional data is collected from various sources, fields, and applications such as medicine, science, business and more to provide helpful information to others. Unfortunately, the complexity of high-dimensional data has made it difficult to interpret and understand. As a result, sophisticated data analysis is required to extract knowledge and information from it. This can be illustrated through a visualization presentation. However, overlap between data can occur during visualization as data increases. Indirectly, it can cause a cluttered visual presentation. As a result, it affects the visual perception of high-dimensional data patterns. High-dimensional data can be deeply explored using dimension arrangement and scaling to overcome it. The arrangement of dimensions is essential since the relationship between these dimensions can influence the existence of an efficient cluster. This dimension is arranged based on the correlation value. The dimension that is more related will be placed next to each other. While performing clusters, dimensions will be scaled in or out. These features are available through Star Coordinate (SC) technique. This paper aims to conduct an exploratory data analysis in the SC environment where users can visualize and interact in a low-dimensional data visualization space. This paper demonstrates data dimensions manipulation's importance in structuring the projected space layout using two data sets. As a conclusion, formation of clusters was crucial and manipulation of data dimensions were essential to structure the projected space layout. The proposed approach has helped users find significant cluster formations by randomizing the scaling and order of dimensions.

This, with the ever-increasing sophistication of cyberwar, calls for novel solutions. In this regard, Large Language Models (LLMs) have emerged as a highly promising tool for defensive and offensive cybersecurity-related strategies. While existing literature has focused much on the defensive use of LLMs, when it comes to their offensive utilization, very little has been reported-namely, concerning Vulnerability Assessment (VA) report validation. Consequentially, this paper tries to fill that gap by investigating the capabilities of LLMs in automating and improving the validation process of the report of the VA. From the critical review of the related literature, this paper hereby proposes a new approach to using the LLMs in the automation of the analysis and within the validation process of the report of the VA that could potentially reduce the number of false positives and generally enhance efficiency. These results are promising for LLM automatization for improving validation on reports coming from VA in order to improve accuracy while reducing human effort and security postures. The contribution of this paper provides further evidence about the offensive and defensive LLM capabilities and therefor helps in devising more appropriate cybersecurity strategies and tools accordingly.