Clustering & Types of following machine learning clustering techniques

Summary

In this article, using Data Science , I will define basic of different types of Clustering algorithms.

Clustering is defined as the algorithm for grouping the data points into collection of groups based on the principle that the similar data points are placed together in one group known as clusters. This clustering methods is categorized as Hard method (in this each data point belongs to max of one cluster) and soft methods(in this data point can belongs to more than one clusters). Also, there is multiple type of clustering methods are present such as Partition Clustering, Hierarchical Clustering, Density-based Clustering, Distribution Model Clustering, Fuzzy Clustering, etc.

Broadly methods of clustering techniques are classified into two types they are Hard methods and soft methods. In the Hard-clustering method, each data point or observation belongs to only one cluster. In the soft clustering method, each data point will not completely belong to one cluster, instead, it can be a member of more than one cluster it has a set of membership coefficients corresponding to the probability of being in a given cluster.

Currently, there are different types of clustering methods in use, here in this article let us see some of the important ones like Hierarchical clustering, Partitioning clustering, Fuzzy clustering, Density-based clustering, and Distribution Model-based clustering. Now let us discuss each one of these with an example:

#machine-learning #analytics-vidhya #data-science #data-visualization #clustering