关键词: 统一计算设备架构（CUDA）, Chameleon层次聚类算法, K-Means聚类算法, 频繁模式树（FP-Tree）, 明尼苏达多项人格测验（MMPI）

Abstract: This paper puts forward a new clustering algorithm AIK-Means. Multiple clustering can be executed within the limited time by using the CUDA technology, which is able to accelerate execution efficiency of the algorithm and optimize the memory method. The Chameleon hierarchical cluster algorithm is used to solve the initial clustering centers sensitive issues of the K-Means algorithm. In order to improve the validity of clustering, the FP-Tree is used for correlation analysis in several clustering results. In this paper, the algorithm is applied to the psychology MMPI test data of a group. The experimental results indicate that the AIK-Means algorithm performs well in the execution efficiency and cluster validity.

Key words: Compute Unified Device Architecture（CUDA）, Chameleon hierarchical cluster algorithm, K-Means algorithm, Frequent Pattern（FP）-Tree, Minnesota Multiphasic Personality Inventory（MMPI）