Clustering based on the In-tree Graph Structure and Affinity Propagation
This is an incremental improvement for clustering methods that use graph-based structures.
The paper tackles the problem of undesired edges in the In-tree graph structure from Nearest Descent clustering by combining it with affinity propagation to automatically remove these edges, demonstrating effectiveness on synthetic and real datasets.
A recently proposed clustering method, called the Nearest Descent (ND), can organize the whole dataset into a sparsely connected graph, called the In-tree. This ND-based Intree structure proves able to reveal the clustering structure underlying the dataset, except one imperfect place, that is, there are some undesired edges in this In-tree which require to be removed. Here, we propose an effective way to automatically remove the undesired edges in In-tree via an effective combination of the In-tree structure with affinity propagation (AP). The key for the combination is to add edges between the reachable nodes in In-tree before using AP to remove the undesired edges. The experiments on both synthetic and real datasets demonstrate the effectiveness of the proposed method.