Clustering based on the In-tree Graph Structure and Affinity Propagation

Abstract

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.

References in corpus (1)

• An Effective Semi-supervised Divisive Clustering Algorithm

Cited by in corpus (5)

• IT-map: an Effective Nonlinear Dimensionality Reduction Method for Interactive Clustering
• Clustering by Descending to the Nearest Neighbor in the Delaunay Graph Space
• Clustering by Deep Nearest Neighbor Descent (D-NND): A Density-based Parameter-Insensitive Clustering Method
• Nonparametric Nearest Neighbor Descent Clustering based on Delaunay Triangulation
• IT-Dendrogram: A New Member of the In-Tree (IT) Clustering Family