In order to perform the implementation of the algorithm, we make use of python package SKlearn.The number of clusters will be determined by the algorithm with respect to data. In contrast to the K-Means clustering algorithm, the output of the Mean Shift algorithm does not depend on assumptions on the shape of the data point and the number of clusters. The data points which try to converge towards the local maxima will be of the same cluster group. The algorithm works by making the data points to attract each other allowing the data points towards the area of high density. KDE utilizes the concept of probability density function which helps to find the local maxima of the data distribution. If no kernel parameter is mentioned, Gaussian Kernel is invoked by default.
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