We presume that the data points match a Gaussian distribution in Gaussian Mixed Models, which is never a constraint in comparison to the restrictions in the prior approaches. Furthermore, this hypothesis can lead to critical cluster shape selection criteria — that is, cluster forms can now be measured. The two most frequent and easy metrics – mean and variance – are used to quantify the data. Expectation-Maximization, a type of optimization function, is used to determine the mean and variance. This function begins with a set of random Gaussian parameters, such as, and checks whether the Hypothesis affirms that a sample belongs to cluster c. After that, we go on to the maximising step, which involves updating the Gaussian parameters to suit the points allocated to the cluster. The goal of the maximisation stage is to increase the probability that the sample belongs to the cluster distribution.