Clustering: Structure Without Labels · seed 1 · A4, ink-friendly. The answer key prints on its own page for grown-ups.

Groups with no right answers attached

Computing · Machine Learning · ages 20-22
Name ______________________   Date ____________

  1. What must you decide before running k-means?

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

  2. 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 add a new centre
    • Sort labels alphabetically, then count them

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

    Circle one:   True   False

  4. 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, stretched data cannot be grouped

  5. 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
    • Different starting centres settled into different final groups

  6. 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
    • The groups look neat in a table

  7. 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 neat round groups that do not exist, so check tight, separated and true
    • Rerun until the groups look rounder

  8. Points sit at 1, 2, 8 and 9, with centres at 1 and 9. After assigning each point to its nearest centre, which points does the first centre hold?

    • 1, 2, 8 and 9
    • 8 and 9
    • 1 and 2
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Answer key

For grown-ups. Fold this page away before handing over the rest.

Groups with no right answers attached W1-mt_BAMGOlWgRY-s1

  1. The number of centres k and the distance to use · K-means cannot start without k and a way to measure nearness.
  2. Assign points to the nearest centre, then move each centre to its group middle · Assign then move, repeated until the groups stop shifting.
  3. True · No labels are given, so resemblance is the only guide.
  4. DBScan, which walks neighbours and can label strays as noise · K-means builds round blobs around centres, while neighbour-walking follows stretched shapes.
  5. Different starting centres settled into different final groups · The starting centres steer where assigning and moving converge.
  6. Groups are tight inside, well separated, and say something true · Finishing proves nothing; tight, separated and true groups prove the result.
  7. K-means can force neat round groups that do not exist, so check tight, separated and true · Finishing only means the steps ran; a wrong k can tidy noise into false blobs.
  8. 1 and 2 · 1 and 2 sit nearer to 1, while 8 and 9 sit nearer to 9.
Worksheet · LightMySky