Unsupervised Learning

Unsupervised learning

In unsupervised learning, the data has no labels; the goal of the algorithm is to find relationships in the data. This system needs to learn without a teacher. For instance, say we have data about a website’s visitors and we want to use it to find groupings of similar visitors. We don’t know and can’t tell the algorithm which group a visitor belongs to; it finds those connections without help based on some hidden patterns in the data. This type of customer segmentation is an example of what is known as clustering — classification with no predefined classes and based on some unknown features.

Another well-known use case is image compression. When saving an image, if we set the palette, say, to 32 colors, clustering will find all the “blueish” pixels, calculate the “average blue,” and set it for all the blue pixels. This helps us achieve a lower file size.

Examples of unsupervised algorithms:

• Clustering: k-Means
• Visualization and dimensionality reduction
• Principal component analysis (PCA), t-distributed
• Stochastic neighbor embedding (t-SNE)
• Association rule learning: Apriori