MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -One of the most important unsupervised learning techniques is clustering, which is the process of partitioning a set of data points according to some measure of similarity (e.g., distance).

Detailed explanation-2: -Clustering can be considered the most important unsupervised learning problem; so, as every other problem of this kind, it deals with finding a structure in a collection of unlabeled data. A loose definition of clustering could be “the process of organizing objects into groups whose members are similar in some way”.

Detailed explanation-3: -Clustering is an unsupervised machine learning task. You might also hear this referred to as cluster analysis because of the way this method works. Using a clustering algorithm means you’re going to give the algorithm a lot of input data with no labels and let it find any groupings in the data it can.

Detailed explanation-4: -“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-5: -Cluster analysis, or clustering, is an unsupervised machine learning task. It involves automatically discovering natural grouping in data. Unlike supervised learning (like predictive modeling), clustering algorithms only interpret the input data and find natural groups or clusters in feature space.