What Is Unsupervised Learning?
Unsupervised learning is commonly used for finding meaningful patterns and groupings inherent in data, extracting generative features, and for exploratory purposes. Imagine you are in a foreign country and you are visiting a food market. You see a stall selling a fruit that you cannot identify. You don’t know the name of this fruit. However, you have your observations to rely on, and you can use these as a reference. In this case, you can easily the fruit apart from nearby vegetables or other food by identifying its various features like its shape, color, or size. This is roughly how unsupervised learning works. Clustering and dimensionality reduction are the most well-known types:
Clustering
Clustering is an unsupervised technique where the goal is to find natural groups or clusters in a feature space and interpret the input data. There are many different clustering algorithms. One common approach is to divide the data points in a way that each data point falls into a group that is similar to other data points in the same group based on a predefined similarity or distance metric in the feature space.
Dimensionality Reduction
Dimensionality reduction is a commonly used unsupervised learning technique where the goal is to reduce the number of random variables under consideration. It has several practical applications. One of the most common uses of dimensionality reduction is to reduce the complexity of a problem by projecting the feature space to a lower-dimensional space so that less correlated variables are considered in a machine learning system