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Unsupervised learning is a method used to enable machines to classify both tangible and intangible objects without providing the machines any prior information about the objects. The things machines need to classify are varied, such as customer purchasing habits, behavioral patterns of bacteria and hacker attacks. The main idea behind unsupervised learning is to expose the machines to large volumes of varied data and allow it to learn and infer from the data. However, the machines must first be programmed to learn from data.
Computer systems need to make sense of large volumes of both structured and unstructured data and provide insights. In reality, it may not be feasible to provide prior information about all types of data that a computer system may receive over a period of time. Keeping this in mind, supervised learning may not be suitable when computer systems need constant information about new types of data. For example, hacking attacks on financial systems or bank servers tend to change their nature and patterns frequently, and unsupervised learning may be more appropriate in such cases since the systems need to be enabled to quickly learn from attack data and infer the kinds of future attacks and suggest preemptive actions.