利用CUDA提高内存数据聚类效能的研究

摘要

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 opti-mize the memory method. The Chameleon hierarchical cluster algorithm is used to solve the initial clustering centers sen-sitive 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 va-lidity.%提出一种新的聚类算法AIK-Means，利用CUDA技术加速算法执行效率，并优化内存方法，可在有限时间内进行多次聚类；将Chameleon层次聚类算法用于解决K-Means算法的初始聚类中心敏感问题；在多次聚类结果中用FP-Tree进行关联分析，提高聚类有效性。将算法应用到某集团心理学MMPI数据测试，实验结果表明AIK-Means算法在执行效率和聚类有效性上具有良好的效果。