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3.  Discuss popular clustering algorithms.
                Ans.  Some popular clustering algorithms are as follows:
                     ●  Centroid-based Clustering: Centroid-based clustering arranges the data into non-hierarchical clusters. K-means
                      clustering is the most popular centroid-based clustering algorithm. Centroid-based algorithms are efficient but easily
                      affected by the initial conditions and outliers.






















                    ●  Density-based Clustering: Density-based clustering groups high density areas into clusters. Hence, arbitrary-shaped
                      distributions occur so that dense areas can be connected. The data points in the separating regions of low density are
                      considered outliers and not assigned to clusters.





















                    ●  Distribution-based Clustering: Distribution-based clustering is a clustering model in which we try to fit the data on
                      the probability that it can belong to the same distribution. The grouping done may be normal or Gaussian. Gaussian
                      distribution is more popular where there are fixed number of distributions. The data is fitted in such a way that the
                      distribution of data gets maximised.
















                    ●  Hierarchical Clustering: Hierarchical clustering builds a tree of clusters. The aim of the algorithm is to produce a
                      tiered series of nested clusters. Each cluster is different from every other cluster, and the objects within each cluster
                      are mostly similar to each other.


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