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Clustering unleashes the power of scRNA-seq through identification of appropriate cell groups. It is considered a pre-requisite to performing differential expression analysis, followed by functional profiling to identify potential biomarkers from scRNA-seq data. Most existing clustering methods either integrate cluster validity indices or need user assistance to identify clusters of arbitrary shape. We develop two clustering methods 1) UIFDBC to identify clusters of arbitrary shapes, 2) UIPBC to cluster scRNA-seq data. Neither method integrates a cluster validity index nor takes any user input. However, specialised approaches are used to benchmark the parameters. Both approaches outperform state-of-the-art methods.