关键词: 聚类, 密度峰, 相似度, [K]近邻

Abstract: For the clustering of density peaks, only the distance between the sample point and the point of pointing （the nearest point of density is bigger than it） is considered, and it is not applicable to the problem of manifold clustering （such as Circleblock data set, Lineblobs data set, etc.）. A density peak clustering algorithm with [K] similarity optimization is proposed. After calculating the density and point of each point, find the [K] neighborhood of each point by the similarity function, and then judge whether the point of the sample point is correct according to the [K] proximity information. For the point pointing to the wrong point, it can effectively reduce the error distribution. Experiments on artificial datasets and UCI datasets show that the new algorithm has a higher accuracy rate.

Key words: clustering, density peaks, similarity, [K] nearest neighbor