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Cluster analysis is a technique used in data analysis and machine learning to identify groups or clusters within a dataset. It is an unsupervised learning method that aims to find similarities and patterns in the data without prior knowledge of the group assignments. The goal of cluster analysis is to partition a dataset into subsets,…
Principal Component Analysis (PCA) is a popular unsupervised learning technique used for dimensionality reduction and feature extraction. PCA transforms a high-dimensional dataset into a lower-dimensional space while retaining the maximum amount of variance in the data. Principal Component Analysis (PCA) works by finding a set of orthogonal vectors, called principal components. It captures the maximum…