| Abstract: | Clustering has long been an important data processing task in different applications. Typically, it attempts to partition the available data into groups according to their
underlying distributions, and each cluster is represented by a center or an exemplar. In
this paper, a new clustering algorithm called gravitational-force-based affinity propagation
(GAP) is proposed, based on the well-known Newton''s law of universal gravitation. It views
the available data points as nodes of a network (or planets of a universe) and the clusters
and their corresponding exemplars can be obtained by transmitting affinity messages based
on the gravitational forces between data points in a network. While GAP is inspired by the
recently proposed affinity propagation (AP) clustering approach, it provides a new definition
of the similarity between data points which makes the AP process more convincing and at
the same time facilitates the differentiation of data points'' importance. The experimental
results show that the GAP clustering algorithm, with comparable clustering accuracy, is
even more efficient than the original AP clustering approach. |
