Unsupervised Learning

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.

 

In supervised learning, we know what output is (X is A, O is B). But, in unsupervised learning, we don't know about the outputs. we just have the data of $x_1$, $x_2$.

 

We can derive this structure by clustering the data based on relationships among the variables in the data.

 

clustering

This clustering is used in many place. (Organize computing cluster, Market segmentation, Social network analysis, Astronomical data analysis etc.)

 

With unsupervised learning there is no feedback based on the prediction results.

 

Example:

 

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.

 

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).

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Q. Of the following examples, which would you address using an unsupervised learning algorithm?

 

A. Given email labeled as spam/not spam, learn a spam filter.

B. Given a set of news articles found on the web, group them into sets of articles about the same stories.

C. Given a database of customer data, automatically discover market segments and group customers into different market segments.

D. Given a dataset of patients diagnosed as either having diabetes or not, learn to classify new patients as having diabetes or not.

 

Ans. B and C