LAUSR

The affinity propagation (AP) clustering algorithm has received a lot of attention over the past few years. AP is efficient and insensitive to initialization, and generates clustering results with lower error and in less time. However, there are still two key limitations: AP-related algorithms cannot identify outliers in clusters. And they are usually not ideal for processing nonlinear data. To address the above issues, we propose a geodesic affinity propagation clustering algorithm based on angle-based outlier factor (ABOF-GAP). First, outliers are identified according to the value of angle-based outlier factor. Besides, Euclidean distance is replaced with geodesic distance to measure similarity. Experiments on synthetic data and real data illustrate the effectiveness of the ABOF-GAP algorithm.