  Measuring similarity between two objects is the core operation in existing
clustering algorithms in grouping similar objects into clusters. This paper
introduces a new similarity measure called point-set kernel which computes the
similarity between an object and a set of objects. The proposed clustering
procedure utilizes this new measure to characterize every cluster grown from a
seed object. We show that the new clustering procedure is both effective and
efficient that enables it to deal with large scale datasets. In contrast,
existing clustering algorithms are either efficient or effective. In comparison
with the state-of-the-art density-peak clustering and scalable kernel k-means
clustering, we show that the proposed algorithm is more effective and runs
orders of magnitude faster when applying to datasets of millions of data
points, on a commonly used computing machine.
