Unsupervised learning is a machine learning concept that analyzes unlabeled and unclassified data to discover hidden knowledge. "The algorithms work on the data without any prior training, but they are constructed in such a way that they can identify patterns, groupings, sorting order, and numerous other interesting knowledge from the set of data.
Application of Unsupervised Learning
Because of its flexibility that it can work on uncategorized and unlabelled data, there are many domains where unsupervised learning finds its application.
Few examples of such applications are as follows:
Segmentation of target consumer populations by an advertisement consulting agency on the basis of few dimensions such as demography, financial data' purchasing habits, etc. so that the advertisers can reach their target consumers efficiently
Anomaly or fraud detection in the banking sector by identifying the pattern of loan defaulters
Image processing and image segmentation such as face recognition, expression identification, etc.
Grouping of important characteristics in genes to identify important influencers in new areas of genetics
Utilization by data scientists to reduce the dimensionalities in sample data to simplify modelling
Document clustering and identifying potential labelling options
Unsupervised learning is now widely used in many areas of Artificial Intelligence (Al) and Machine Learning. Chat bots, self-driving cars, and a slew of other recent innovations are the result of combining unsupervised and supervised learning. So, in this chapter, we will discuss two major aspects of unsupervised learning: Clustering, which aids in the segmentation of a set of objects into groups of similar objects, and Association Analysis, which is concerned with the identification of relationships between objects in a data set.