What is Clustering ?
Introduction to Clustering
Clustering is the most important technique of unsupervised learning. Clustering is an unsupervised learning technique in which there is predefined classes and prior information which defines how the data should be grouped or labeled into separate classes. Cluster is the collection of data objects which are similar to one another within the same group (class or category) and are different from the objects in the other clusters. It is Exploratory Data Analysis (EDA) process which helps to discover hidden patterns of interest or structure in data. Clustering can also work as a standalone tool to get the insights about the data distribution or as a preprocessing step in other algorithms.
High quality clusters can be created by reducing the distance between the objects in the same cluster known as intra-cluster minimization and increasing the distance with the objects in the other cluster known as inter-cluster maximization.
Intra-cluster minimization: The closer the objects in a cluster, the more likely they belong to the same cluster.
Inter-cluster Maximization: This makes the separation between two clusters. The main goal is to maximize the distance between 2 clusters.
