Unsupervised Learning is a type of Machine Learning (ML) where an algorithm learns patterns and structures in input data without being explicitly provided with labeled output. Unlike supervised learning, where the algorithm learns from labeled examples, unsupervised learning focuses on finding hidden structures, relationships, and patterns within the data itself.
In unsupervised learning, the algorithm works with unlabelled data and aims to uncover underlying insights or groupings. The primary goal is to explore the data’s inherent structure and gain a deeper understanding of its characteristics.
There are two main categories of unsupervised learning:
Clustering: Clustering involves grouping similar data points together based on certain features or characteristics. The algorithm identifies clusters in the data, where data points within the same cluster are more similar to each other than to data points in other clusters. Common clustering algorithms include k-means clustering, hierarchical clustering, and DBSCAN.
Dimensionality Reduction: Dimensionality reduction techniques aim to reduce the number of features or dimensions in a dataset while retaining as much meaningful information as possible. This can help in visualizing and understanding high-dimensional data and improving computational efficiency. Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) are examples of dimensionality reduction techniques.
Unsupervised learning is used for various purposes, such as:
Unsupervised learning can be more challenging than supervised learning since there are no predefined labels to guide the algorithm. The quality of the results often relies on the algorithm’s ability to find meaningful patterns and relationships in the data. It is particularly useful when working with large and complex datasets where manual labeling may be impractical or unavailable.