When an analyst studies some data and develops a hypothesis after viewing the results to explain the observations, the analyst is using unsupervised data mining
This is further explained below.
What is unsupervised data mining?
Generally, The use of labeled datasets is the primary point of difference between the two methods.
To put it another way, the unsupervised learning algorithm doesn't really make use of labeled input and output data, in contrast to supervised learning, which does.
In conclusion, Unsupervised data mining refers to the process in which an analyst examines certain data, views the findings of the analysis, and then formulates a hypothesis to explain the observations based on those results.
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