Definition
Can a shop group its customers without knowing anything about them?
Unsupervised learning is machine learning from data that has no labels. Instead of predicting answers, the model finds structure in the data by itself, such as groups of similar items, a task called clustering.
In simple words
Pour a box of mixed buttons on a table and sort them into piles by colour and size. Nobody told you the piles; you found them. In the activity, the computer does this with shop customers: it notices that some visit often and spend little, others rarely visit but spend a lot, and groups them.
Why it matters
Labels are expensive: someone has to write the right answer for every example. Unsupervised learning finds patterns without them. Shops use it to understand customers, music apps to group similar songs, and banks to spot unusual activity.
How it works
- Decide how many groups to look for.
- Place a centre for each group.
- Each item joins the nearest centre.
- Each centre moves to the middle of its items.
- Repeat until nothing changes. The groups are found.
Try it yourself
Group mystery customers by similarity
Choose how many groups, place the centres, then step through the rounds until the groups settle.
Common mistakes
- Expecting the computer to name the groups. People interpret them.
- Thinking there is one correct number of groups. Different choices show different patterns.
- Mixing it up with supervised learning. No labels are given here.
Check yourself
Summary
- Unsupervised learning finds patterns in data with no labels.
- Clustering groups similar items together.
- People interpret what the groups mean.
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