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The k-Means (KM) algorithm is a distance-based clustering algorithm that partitions the data into a predetermined number of clusters (provided there are enough distinct cases).
Distance-based algorithms rely on a distance metric (function) to measure the similarity between data points. The distance metric is either Euclidean, Cosine, or Fast Cosine distance. Data points are assigned to the nearest cluster according to the distance metric used.
Use a Clustering Node to build KM models.
Use an Apply Node to apply a KM model to new data.
The following topics describe KM models: