MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -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.

Detailed explanation-2: -Some examples of unsupervised learning algorithms include K-Means Clustering, Principal Component Analysis and Hierarchical Clustering.

Detailed explanation-3: -Some use cases for unsupervised learning-more specifically, clustering-include: Customer segmentation, or understanding different customer groups around which to build marketing or other business strategies. Genetics, for example clustering DNA patterns to analyze evolutionary biology.

Detailed explanation-4: -Four common unsupervised tasks inclused clustering, visualization, dimensionality reduction, and association rule learning.