Introduction
The unsupervised machine learning method focuses on revealing the hidden structure of unlabeled data. A key difference between unsupervised learning and supervised learning is that the latter method employs labeled data as learners. Therefore, one can evaluate the model based on known answers. In contrast, one cannot evaluate unsupervised learning as it does not have any known answers. Mostly, unsupervised learning focuses on two main areas: clustering and dimension reduction.
Clustering is a technique used to group similar objects (close in terms of distance) together in the same group (cluster). Clustering analysis does not use any label information, but simply uses the similarity between data features to group them into clusters.
Dimension reduction is a technique that focuses on removing irrelevant and redundant data to reduce the computational cost and avoid overfitting; you can reduce the features into a smaller subset without a significant loss of information. Dimension...
