关键词: 聚类, 密度, 共享近邻, 亲和度, 数据挖掘

Abstract: In order to solve the problem of inaccurate clustering results when dealing with high-dimensional and multi-density datasets, a Shared Nearest Neighbor Affinity（SNNA） based clustering algorithm is put forward. The algorithm incorporates [k] nearest neighbor and shared nearest neighbor, and defines shared neighbor affinity as the local density measure of the object. The algorithm firstly extracts the core points according to the affinity, then uses the breadth first search algorithm to cluster the core points, and finally assigns the non-core points to the right cluster to complete the clustering of the whole data set. Experimental results show that the algorithm can find clusters of arbitrary shape, size and density. Compared with other similar algorithms, SNNA has higher clustering accuracy when dealing with high-dimensional data.

Key words: clustering, density, shared nearest neighbor, affinity, data mining