Lesson 5 — Machine Learning and Mining by Problem Type
Classify methods by the problem and learning signal, not by how advanced the technique sounds.
Supervised / Unsupervised / Reinforcement
- Supervised: known/labeled target. Example: emails labeled spam/not-spam train a classifier.
- Unsupervised: no target labels; discover hidden structure. Example: natural customer groups.
- Reinforcement: actions improve through reward/feedback toward a goal.
Fast discriminator: What tells the algorithm it is doing well?
Mining-method selector
| Technique | Best clue |
|---|---|
| Profiling | understand typical structure/content/patterns |
| Data reduction | make a large/complex set smaller while retaining important information |
| Association | find items/events that occur or relate together |
| Clustering | group similar observations/cases |
| Self-organizing map | organize/visualize complex multidimensional similarity |
| Text mining | analyze/classify content in text/documents |
| Sentiment analysis | infer attitude/opinion/feeling from language in context |
Association vs clustering
“Which products occur together?” → association.
“Which customers are naturally similar?” → clustering.
Mining vs prediction
Data mining can discover relationships you did not know to ask about. Predictive analytics estimates a specified likely future outcome. Mining can feed a predictive model, but they are not identical.
Sentiment trap
A positive word does not guarantee positive sentiment. Negation, sarcasm, and surrounding context make naive keyword counts unreliable.
Source: pp. 479–484.