  This paper proposes a simple but effective graph-based agglomerative
algorithm, for clustering high-dimensional data. We explore the different roles
of two fundamental concepts in graph theory, indegree and outdegree, in the
context of clustering. The average indegree reflects the density near a sample,
and the average outdegree characterizes the local geometry around a sample.
Based on such insights, we define the affinity measure of clusters via the
product of average indegree and average outdegree. The product-based affinity
makes our algorithm robust to noise. The algorithm has three main advantages:
good performance, easy implementation, and high computational efficiency. We
test the algorithm on two fundamental computer vision problems: image
clustering and object matching. Extensive experiments demonstrate that it
outperforms the state-of-the-arts in both applications.

Computer Vision and Pattern Recognition;Social and Information Networks;Machine Learning