Which of the following is an internal method for evaluating alternative clusterings produced by K-Means algorithm?

Question

BlackTom AI · Accepted Answer

When comparing different clusterings produced by K-Means, you want metrics that reflect how well the data points fit within their assigned clusters, using information intrinsic to the clustering result itself.
Option 1: 'Compare the sum of squared error differences between instances and their corresponding cluster centers for each alternative clustering.' This aligns with the intra-cluster cohesion concept (often called within-cluster sum of squares or inertia). Lower SSE indicates tighter, more compact clusters, which is a standard internal criterion for K-Means evaluation.
Option 2: 'Use a production rule generator to compare the rule sets generated for each clustering.' This introduces an external, rule-based representation not inherent to the clustering process; it does not measure clustering quality and is not an internal evaluation method for K-Means.
Option 3: 'Compute and compare class resemblance scores for the clusters formed by each clustering.' Class resemblance scores imply supervised or labeled class matching, which is not available in unsupervised clustering unless ground-truth labels are provided. Since these are internal evaluations, this is not appropriate for purely internal comparison without labels.
Option 4: 'Create and compare the decision trees determined by each alternative clustering.' Decision trees are supervised models and not a direct output of K-Means clustering; using them to compare clusterings adds unrelated structure and external modeling, making it unsuitable as an internal evaluation method for K-Means results.

COS10022 Lecture 3_Knowledge Check

Which of the following is an internal method for evaluating alternative clusterings produced by K-Means algorithm?

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