→ In k-means clustering, k represents the number of clusters that the algorithm will attempt to form. The algorithm partitions the dataset into k distinct, non-overlapping clusters based on feature similarity. Each cluster has a centroid, and the algorithm aims to minimize the intra-cluster variance.
Why the other options are incorrect:
A: Number of tests is unrelated to the k-means algorithm.
B: Data splits refer to cross-validation or train/test splits, not k in k-means.
D: Distance between features is computed during clustering but is not what "k" represents.
Official References:
CompTIA DataX (DY0-001) Official Study Guide – Section 4.2:“In k-means clustering, k denotes the number of clusters into which the dataset will be partitioned.”
Introduction to Machine Learning, Chapter 6:“The 'k' in k-means specifies how many groupings the algorithm will seek to discover based on proximity in feature space.”
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