Unsupervised learning is a type of machine learning where the algorithm learns from data without being explicitly told what the correct output should be. The goal is to find patterns and relationships in the data without any prior knowledge or guidance.
In unsupervised learning, the algorithm learns patterns and relationships from unlabelled data without any external guidance. The goal of unsupervised learning is to find structure in the data, which can then be used for tasks like clustering, anomaly detection, and dimensionality reduction.
There are two main types of unsupervised learning:
One common type of unsupervised learning is clustering, where the algorithm groups similar data points together based on their features or characteristics. There are several different clustering algorithms, including:
K-means clustering is a popular method for clustering data. The algorithm aims to partition the data into k clusters, where k is a predefined value. The algorithm works by iteratively assigning each data point to the nearest cluster centroid and then updating the centroids based on the mean of the data points assigned to the cluster. This process is repeated until convergence.
Hierarchical clustering is a method that creates a hierarchy of clusters based on the similarity between data points. The algorithm works by initially considering each data point as a separate cluster and then iteratively merging the closest clusters based on some similarity metric until all data points belong to a single cluster.
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a clustering method that groups together data points that are close to each other in high-density regions and marks as noise points that lie in low-density regions. The algorithm works by identifying core points, which have a minimum number of neighboring points within a specified radius, and then expanding the cluster by adding nearby points to the cluster.
Clustering can be understood process of grouping similar data points together. It involves finding the natural structure of the data and dividing it into clusters or groups. Some popular clustering algorithms include k-means, hierarchical clustering, and DBSCAN.
Dimensionality reduction is the process of reducing the number of features in the data while preserving the maximum amount of information. This is useful for visualizing high-dimensional data or reducing the computational cost of training a model. Some popular dimensionality reduction algorithms include principal component analysis (PCA), t-SNE, and autoencoders.
This can be useful for several reasons, including:
Common dimensionality reduction techniques include Principal Component Analysis (PCA) and t-SNE.
Unsupervised learning has a wide range of applications, including: