Robust的分布式k中心聚类算法的研究与实现

并行处理的研究在数据挖掘中是十分必要的。在理论分析的基础上，提出在对经典串行PAM算法进行并行时应如何从局部聚类信息生成完备的全局聚类信息，据此提出了算法DPAM，在提高计算性能的同时，使聚类质量等价于相应串行PAM算法。为提高并行算法的执行效率，还介绍了如何减小计算结点间通信的代价。最后对提出的算法进行性能分析和实验，说明该算法是高效可行的。

Robust distributed k-mediods clustering algorithm

Parallel is very important in data mining.This paper proposes a distributed k-mediods clustering algorithm by analyzing how to get satisfactory clustering information from local information.Its quality is equivalent to serial PAM algorithm but its calculation performance is higher.The paper gives still the way that improves the efficiency of parallel PAM algorithm by reducing the cost of communication,analyzes the performance of the algorithm and gives the result of experiment.This explains that the algorithm is effective and reliable.