Spherical k-means is an unsupervised clustering algorithm where the lengths of all vectors being compared are normalized to 1, so that they differ in direction but not in magnitude.

This algorithm is used when the magnitude of the vectors is irrelevant or not particularly important, especially when dealing with high-dimensional data.

It works by measuring the angles between the vectors (cosine similarity) rather than using the Euclidean distance as in the standard k-means algorithm.

For example, if you have a dataset of text documents represented as term frequency vectors, spherical k-means can cluster these documents based on their content similarity, ignoring the document lengths.

In summary, spherical k-means is efficient for clustering high-dimensional data where vector magnitude is not significant. Hence, it provides a more suitable alternative to standard k-means in specific scenarios.