A k-ap clustering algorithm based on manifold similarity measure

Abstract

K-AP clustering algorithm is a kind of affinity propagation (AP) clustering that can directly generate specified K clusters without adjusting the preference parameter. Similar to AP clustering algorithm, the clustering process of K-AP algorithm is also based on the similarity matrix. How to measure the similarities of data points is very important for K-AP algorithm. Since the original Euclidean distance is not suit for complex manifold data structure, we design a manifold similarity measurement and proposed a K-AP clustering algorithm based on the manifold similarity measure (MKAP). If two points lie on the same manifold, we assume that there is a path inside the manifold to connect the two points. The manifold similarity measure uses the length of the path as the manifold distance between the two points, so as to compress the distance of the data points in high-density region, while enlarge the distance of data points in low-density region. The clustering performance of the proposed MKAP algorithm is tested by comprehensive experiments. The clustering results show that MKAP algorithm can well deal with the datasets with complex manifold structures.