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K-means clustering is a method for identifying groups of similar observations, i.e. cells or samples. K-means clustering aims to group observations into a pre-determined number of clusters (k) so that each observation belongs to the cluster with the nearest mean. An important aspect of K-means clustering is that it expects clusters to be of similar size (equal variance) and shape (distribution of variance is spherical). The Compare Clusters task can also be performed to help determine the optimal number of K-means clusters. 

Running K-means clustering

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K-means clustering produces a K-means Clusters result data node; double-click to open the task report which lists the cluster statistics (Figure 2). If Compute biomarkers was enabled, top markers will be available by double-clicking the Biomarkers result data node. If clustering was run with Split cells by sample enabled on a single cell counts data node, the cluster results table displays the number of clusters found for each sample and clicking the sample name opens the sample-level report. 

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