Geodesic Affinity Propagation Clustering Based on Angle-Based Outlier Factor

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