In k-means clustering, what is the primary goal of the algorithm? (you can choose more than one)

Question

BlackTom AI · Accepted Answer

This question asks about the main objective of k-means clustering, noting that more than one option may be correct.
Option a: 'To partition the data into clusters such that intra-cluster similarity is high and inter-cluster similarity is low.' This statement aligns with the core goal of k-means: create compact, well-separated clusters by minimizing within-cluster variance, which translates to high similarity inside clusters and low similarity between clusters.
Option b: 'To maximise the distance between all data points.' This is incorrect because k-means does not aim to maximize pairwise distances overall. In fact, it tries to keep points within a cluster close to the cluster centroid, reducing intra-cluster dispersion rather than maximizing global distances.
Option c: 'To identify outliers in the dataset.' While k-means can sometimes reveal outliers (points far from centroids), identifying outliers is not the primary objective of the algorithm. Outliers are typically handled by separate methods or robust variants, not by the fundamental clustering goal.
Option d: 'To reduce the dimensionality of the dataset.' Dimensionality reduction is a different class of techniques (e.g., PCA, t-SNE). K-means operates in the original feature space to form clusters and does not inherently reduce dimensionality.
Putting these pieces together, the statements that describe the primary purpose are those about forming cohesive, distinct groups rather than maximizing all distances, flagging outliers, or reducing dimensions.

In k-means clustering, what is the primary goal of the algorithm? (you can choose more than one)

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