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Clustering involves the grouping of data points. K-Means clustering is the well known clustering algorithm. K-means starts with a random choice of cluster centers. Hence it yields different clustering outcomes on various runs of the algorithm. Mean shift clustering is a sliding-window based algorithm that is used to find dense areas of data points. It is a centroid-based algorithm. I.e. it mains at locating the center points of each group which will work by updating candidates for center points within the sliding window.

Hierarchical clustering algorithms consist of 2 categories: Top-down and bottom-up. The bottom-up algorithm treats every single data point as a single cluster at the outset and then merge pairs of groups until all groups have been combined into a single cluster.

Expectation maximization cluster uses a Gaussian mixture model. This method is used to determine the parameters of a Gaussian. https://www.ims.uni-stuttgart.de/institut/mitarbeiter/schulte/theses/phd/algorithm.pdf.