FUNDAMENTALS OF COMPUTER

Detailed explanation-1: -Hierarchical clustering, also known as hierarchical cluster analysis (HCA), is an unsupervised clustering algorithm that can be categorized in two ways; they can be agglomerative or divisive.

Detailed explanation-2: -“Clustering” is the process of grouping similar entities together. The goal of this unsupervised machine learning technique is to find similarities in the data point and group similar data points together. Why use Clustering? Grouping similar entities together help profile the attributes of different groups.

Detailed explanation-3: -K-means is one of the simplest unsupervised learning algorithms that solves the well known clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed a priori.

Detailed explanation-4: -Clustering is an unsupervised learning technique, which groups unlabeled data points based on their similarity and differences. Hence, points are grouped into clusters in such a way that those in a same group have the most similarity with each other, while points in different groups have little to no similarities.

Detailed explanation-5: -1. Clustering-Unsupervised Learning. Clustering is the method of dividing the objects into clusters that are similar between them and are dissimilar to the objects belonging to another cluster. For example, finding out which customers made similar product purchases.