Visual methods were used for pre-cluster assessment and useful cluster partitions. Existing visual methods, such as visual assessment tendency (VAT), spectral VAT (SpecVAT), cosine-based VAT (cVAT), and multi-viewpoints cosine-based similarity VAT (MVS-VAT), effectively assess the knowledge about the number of clusters or cluster tendency. Tweets data partitioning is underlying the problem of social data clustering. Cosine-based visual methods succeeded widely in text data clustering. Thus, cVAT and MVS-VAT are the best suited methods for the derivation of social data clusters. However, MVS-VAT is facing the problem of scalability issues in terms of computational time and memory allocation. Therefore, this paper presents the sampling-based MVS-VAT computing technique to overcome the scalability problem in social data clustering to select sample inter-cluster viewpoints. Standard health keywords and benchmarked TREC2017 and TREC2018 health keywords are taken to extract health tweets in the experiment for illustrating the performance comparison between existing and proposed visual methods.

视觉方法用于群集前评估和有用的群集分区。现有的视觉方法，例如视觉评估趋势（VAT），频谱增值税（SpecVAT），基于余弦的增值税（cVAT）和基于多角度余弦的相似性增值税（MVS-VAT），可以有效地评估有关聚类或聚类趋势。推文数据分区是社交数据集群问题的根本。基于余弦的视觉方法在文本数据聚类中大获成功。因此，cVAT和MVS-VAT是派生社交数据集群的最合适方法。但是，MVS-VAT在计算时间和内存分配方面面临可伸缩性问题。因此，本文提出了基于样本的MVS-VAT计算技术，以克服社交数据聚类中的可伸缩性问题，从而选择样本集群间观点。在实验中，使用标准健康关键字以及基准的TREC2017和TREC2018健康关键字来提取健康推文，以说明现有视觉方法和拟议视觉方法之间的性能比较。