start with the idea before the implementation.
the mechanisms you need to reason about.
Distance metrics
Distance metrics is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
K-means
K-means is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
Density clustering
Density clustering is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
Cluster validation
Cluster validation is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
turn the lesson into evidence.
standardize features
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
run multiple k values
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
profile each cluster
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
prove you can explain and decide.
justify the distance metric
ask cortex to test me →test stability
ask cortex to test me →connect clusters to action
ask cortex to test me →what usually goes wrong.
treating clusters as truth
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
ignoring scaling
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
choosing k from aesthetics
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
Create a short Clustering engineering note with one working artifact one metric one failure case and one decision about when you would or would not use it.
Save the result in your portfolio or project repository. A strong learning artifact should make your assumptions, metrics and failure analysis visible.