  We present a novel algorithm, called Links, designed to perform online
clustering on unit vectors in a high-dimensional Euclidean space. The algorithm
is appropriate when it is necessary to cluster data efficiently as it streams
in, and is to be contrasted with traditional batch clustering algorithms that
have access to all data at once. For example, Links has been successfully
applied to embedding vectors generated from face images or voice recordings for
the purpose of recognizing people, thereby providing real-time identification
during video or audio capture.
