Unsupervised learning algorithms are a type of machine learning algorithm that do not require labeled data for training. Instead, they attempt to find patterns or structure in the input data without any explicit guidance or supervision.
There are several types of unsupervised learning algorithms, including clustering, anomaly detection, and dimensionality reduction.
Clustering algorithms group similar data points together into clusters, based on the similarity of their features. This can be useful for tasks such as customer segmentation, where we want to group customers based on their purchasing behavior or demographics.
Anomaly detection algorithms identify data points that are significantly different from the rest of the data. This can be useful for tasks such as fraud detection, where we want to identify unusual patterns in financial transactions.
Dimensionality reduction algorithms reduce the number of features in the data, while retaining as much of the important information as possible. This can be useful for tasks such as visualizing high-dimensional data or reducing the computational complexity of other machine learning algorithms.
Unsupervised learning algorithms are often used in exploratory data analysis, where we want to gain insights into the underlying structure of the data without any preconceptions. They can also be used in combination with supervised learning algorithms, such as in semi-supervised learning, where we use labeled data to guide the clustering or dimensionality reduction of the unlabeled data.