What must you decide before running k-means?

• The number of centres k and the distance to use
• The correct labels for every single point in advance
• The file name of the dataset

What two steps does k-means alternate?

• Assign points to the nearest centre, then move each centre to its group middle
• Delete the farthest point, then grow a new centre from all the remaining points
• Sort labels alphabetically, then count them

Clustering starts with no target column, asking only which examples resemble each other.

Your data forms long stretched groups with stray points. Which tool fits better?

• K-means, since it loves stretched shapes
• DBScan, which walks neighbours and can label strays as noise
• No algorithm fits stretched data, so grouping is hopeless here

Two runs on the same points end with different groups. What best explains it?

• The points moved between runs
• K-means always returns the same groups on every run from any start
• Different starting centres settled into different final groups

What makes a clustering result meaningful rather than merely tidy?

• Groups are tight inside, well separated, and say something true
• The algorithm finished without errors, which proves the groups real
• The groups look neat in a table

A friend says the groups must be real because the algorithm finished. What is the best reply?

• True, finished output is always meaningful
• K-means can force round groups that are not real
• Rerun from fresh starts until the groups look rounder

Points sit at 2, 4, 10 and 12, with starting centres at 2 and 12. After one round of assigning points and moving centres, where does the first centre sit?