Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don't necessarily know the effect of the variables.
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We can derive this structure by clustering the data based on relationships among the variables in the data.
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With unsupervised learning there is no feedback based on the prediction results.
Unsupervised learning is a branch of machine learning that learns from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning identifies commonalities in the data and reacts based on the presence or absence of such commonalities in each new piece of data.
Example:
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Clustering: Take a collection of 1,000,000 different genes, and find a way to automatically group these genes into groups that are somehow similar or related by different variables, such as lifespan, location, roles, and so on.
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Non-clustering: The "Cocktail Party Algorithm", allows you to find structure in a chaotic environment. (i.e. identifying individual voices and music from a mesh of sounds at a cocktail party).
In clustering problems we split the training examples by unknown characteristics. The algorithm itself decides what characteristic to use for splitting.
💻 Usage examples: market segmentation, social networks analysis, organize computing clusters, astronomical data analysis, image compression, etc.