Cluster analysis aims to create the groups for the data objects based on the assessment of similarity features. It is an essential unsupervised technique for the unlabelled datasets. For example, data clustering methods' primary problem is that k-means suffer from the intractable assignment of 'k' value by external interference (or user). Finding the number of clusters 'k' is called a clustering tendency. Existing visual approaches, i.e., visual access tendency (VAT), cosine-based VAT (cVAT), cosine-based spectral VAT(CS-VAT), are suitable for determining the value of cluster tendency of regular data. The Clustering using Improved Visual Assessment of Tendency (ClusiVAT) performs as the best for significant data clustering than other visual approaches. It uses the sampling technique for faster results; however, it perfectly works for Gaussian-based generated datasets. Thus, the proposed work develops the enhanced visual approaches for obtaining the quality of clusters for the typical datasets. Performance of enhanced visual approaches is demonstrated in the experimental study using benchmarked datasets.

聚类分析旨在基于相似性特征的评估为数据对象创建组。对于未标记的数据集，这是一项必不可少的无监督技术。例如，数据聚类方法的主要问题是，k均值受到外部干扰（或用户）对“ k”值的棘手分配。找到聚类数“ k”称为聚类趋势。现有的视觉方法，即视觉访问趋势（VAT），基于余弦的VAT（cVAT），基于余弦的频谱VAT（CS-VAT），适用于确定常规数据的聚类趋势值。所述的CLU的TeringBay使用我mproved V isual甲的ssessment Ť与其他可视化方法相比，高端性（ClusiVAT）表现最佳，可用于重要数据聚类。它使用采样技术获得更快的结果。但是，它完全适用于基于高斯的生成数据集。因此，提出的工作开发了增强的可视化方法，以获取典型数据集的聚类质量。使用基准数据集的实验研究证明了增强视觉方法的性能。