MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Clustering is the most common unsupervised learning technique. It is used for exploratory data analysis to find hidden patterns or groupings in data. Applications for cluster analysis include gene sequence analysis, market research, and object recognition.

Detailed explanation-2: -The most common unsupervised learning method is cluster analysis, which applies clustering methods to explore data and find hidden patterns or groupings in data. With MATLAB you can apply many popular clustering algorithms: Hierarchical clustering: Builds a multilevel hierarchy of clusters by creating a cluster tree.

Detailed explanation-3: -Clustering algorithms: for anomaly detection and market segmentation. From all unsupervised learning techniques, clustering is surely the most commonly used one. This method groups similar data pieces into clusters that are not defined beforehand.

Detailed explanation-4: -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-5: -The most commonly used unsupervised learning algorithms are: K-means clustering. Hierarchical clustering. Apriori algorithm.