To understand the Unsupervised Learning, it is suggested
to be getting familiar with Supervised learning from HERE.
Unsupervised learning is the opposite of supervised
learning, because a training set is not available hence data is used without
labeled and we are unsure about the output.
Unsupervised learning can understand the data patterns and finds
structures in that.
Unsupervised
learning algorithms are used to group cases based on similar attributes of data
set. These models also are referred to as self-organizing maps.
Real
Example: In android phone, Google Photos app has a feature which can categorize
and makes an album of the photo based on the person. Well, its image processing
algorithm is working, but the app is grouping the person with similar face
patterns in multiple photos.
Popular clustering techniques in Unsupervised learning include:
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