关键词: 高维, 共享最近邻, 子空间聚类, 闭频繁项集

Abstract: According to the measurement results in high dimensional data is not obvious problems of the traditional distance, proposes a shared nearest neighbor subspace clustering algorithm （SNN_SC）, according to the dimension of the data set into multiple nearest neighbor transaction database mining in transaction database maximum co-occurrence object set, namely dimension clustering. On the one dimensional clustering set, the closed frequent itemsets are further exploited. The dimension of the closed frequent itemsets is a subspace. The experimental results show that SNN_SC can more accurately locate the subspace, and generate a complete clustering in subspace.

Key words: high dimensional, shared nearest neighbor, subspaceclustering, closed frequent items