Data Mining Practice Test

k-means operates in numeric feature space, using distances between points and a cluster centroid defined as the mean of points in the cluster. Those computations require numeric values for each attribute. If an attribute is categorical, distance and averaging aren’t meaningful without converting the category into numbers first (for example, with one-hot encoding). After such encoding, features become numeric, and k-means can be applied. Text data, similarly, must be transformed into numeric features (like TF-IDF) before using k-means. So the data type for attributes used in a k-means model is numeric.