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 clustering (GFAPC) 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 GFAPC is inspired by the recently proposed affinity propagation clustering (APC) approach, it provides a new definition of the similarity between data points which makes the APC process more convincing and at the same time facilitates the differentiation of data points' importance. The experimental results show that the GFAPC algorithm, with comparable clustering accuracy, is even more efficient than the original APC approach.
|Number of pages||15|
|Journal||Journal of Algorithms and Computational Technology|
|Publication status||Published - 1 Mar 2011|
ASJC Scopus subject areas
- Numerical Analysis
- Computational Mathematics
- Applied Mathematics