“…Data Clustering is an important part of exploratory data analysis (EDA), which aims to summarize and visualize characteristics of eHealth data for the creation of groups with an estimated similarity features of data objects, and it is an emerging area to derive the clusters in real-time applications like, health care text data clustering (Lin et al, 2014), biological clustering (Ronan et al, 2016), image and video mining (Gilbert and Bowden, 2017), health-care applications (Nallamala et al, 2019;Vamsidhar et al, 2018), information retrieval (Jahnavi et al, 2019;Varish and Pal, 2020), sentiment analysis (Devisetty, 2019), e-commerce application (Ismail et al, 2018) and multimedia application (Srinivas and Ismail, 2018). Top clustering methods (Wu et al, 2008), k-means (Low et al, 2019;Peng and Liu, 2019;Kiran, 2018a;Kiran, 2018b) and graph-based clustering (Duda et al, 2000) perform the data clustering (Ramathilagam et al, 2013) with pre-assigned "k" value. Deriving the "k" value is known as cluster tendency.…”