A mutual neighbor-based clustering method and its medical applications

Clustering analysis has been widely used in various real-world applications. Due to the simplicity of K-means, it has become the most popular clustering analysis technique in reality. Unfortunately, the performance of K-means heavily relies on initial centers, which should be specified in prior. Besides, it cannot effectively identify manifold clusters. In this paper, we propose a novel clustering algorithm based on representative data objects derived from mutual neighbors to identify different shaped clusters. Specifically, it first obtains mutual neighbors to estimate the density for each data object, and then identifies representative objects with high densities to represent the whole data. Moreover, a concept of path distance, deriving from a minimum spanning tree, is introduced to measure the similarities of representative objects for manifold structures. Finally, an improved K-means with initial centers and path-based distances is proposed to group the representative objects into clusters. For non-representative objects, their cluster labels are determined by neighborhood information. To verify the effectiveness of the proposed method, we conducted comparison experiments on synthetic data and further applied it to medical scenarios. The results show that our clustering method can effectively identify arbitrary-shaped clusters and disease types in comparing to the state-of-the-art clustering ones.