Unsupervised Learning

Unsupervised Learning is a type of machine learning where the system tries to learn from data without being told what to look for or what the results should be. It’s like giving a kid a huge box of LEGO bricks without the instruction manual; they start to group bricks by color or size just because they notice similarities. In business intelligence and data analytics, unsupervised learning helps uncover hidden patterns or groupings in data that might not be immediately obvious.

Businesses use unsupervised learning for things like customer segmentation, which is the process of grouping customers with similar traits without knowing in advance what those groups might be. It can help discover natural structures and relationships in complex data. Imagine a grocery store using unsupervised learning to analyze shopping patterns; they might find out that people who buy organic vegetables also tend to buy gluten-free products, leading to better store layout and targeted promotions.

Because unsupervised learning doesn’t require labeled data — data that’s already been sorted and tagged by humans — it can work with the vast amounts of unstructured data that companies collect. This approach provides a way for businesses to make sense of this data and gain insights that can lead to more informed decision-making, innovative marketing strategies, and improved customer experiences.