Which of the descriptions listed below is TRUE?

Question

BlackTom AI · Accepted Answer

The question asks to identify which description among the options is true about K-means.
Option 1: K-means is a clustering method. This is correct because K-means is a popular unsupervised learning algorithm used to partition data into K distinct clusters by minimizing within-cluster variance. It groups similar data points together without using labeled outcomes.
Option 2: K-means is a classification method. This is incorrect because classification requires labeled data and assigns discrete class labels to new observations; K-means does not rely on class labels and does not perform predictive labeling in the supervised sense.
Option 3: K-means is a regression method. This is false since regression involves predicting a continuous target variable based on input features with a relationship model; K-means does not fit a predictive function to map inputs to a numeric target.
Option 4: K-means is an object tracking method. This is inaccurate because object tracking typically involves temporal data and computer vision techniques to follow objects across frames; K-means operates solely on feature vectors for clustering and does not perform tracking by itself.

COS10022 Lecture 3_Knowledge Check

Which of the descriptions listed below is TRUE?

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