In unsupervised learning, a person feeds a machine a large amount of information, asks a question, and then the machine is left to figure out how to answer the question by itself.
For example, the machine might be fed many photos and articles about dogs. It will classify and cluster information about all of them. When shown a new photo of a dog, the machine can identify the photo as a dog, with reasonable accuracy.
Unsupervised learning occurs when the algorithm is not given a specific “wrong” or “right” outcome. Instead, the algorithm is given unlabeled data.
Unsupervised learning is helpful when you don’t know how to classify data. For example, imagine you work for a banking institution and you have a large set of customer financial data. You don’t know what type of groups or categories to organize the data. Here, an unsupervised learning algorithm could find natural groupings of similar customers in a database, and then you could describe and label them.
This type of learning has the ability to discover similarities and differences in information, which makes it an ideal solution for exploratory data analysis, cross-selling strategies, customer segmentation, and image recognition.