CAJ | 학술논문

为提高聚类算法效率，提出一种基于动态云平台的快速闭树聚类并行算法。针对云计算平台Hadoop中任务的随机分配策略，给出一个满足最小化消耗成本的任务分配算法 CDA-GA，并基于该算法提出动态云平台模型。将传统的频繁闭树挖掘算法与聚类算法并行化，应用于动态云平台中，设计基于动态云平台的闭树聚类算法框架。实验结果表明，该算法有效可行，适合在大规模数据下进行聚类分析。
위제고취류산법효솔，제출일충기우동태운평태적쾌속폐수취류병행산법。침대운계산평태Hadoop중임무적수궤분배책략，급출일개만족최소화소모성본적임무분배산법 CDA-GA，병기우해산법제출동태운평태모형。장전통적빈번폐수알굴산법여취류산법병행화，응용우동태운평태중，설계기우동태운평태적폐수취류산법광가。실험결과표명，해산법유효가행，괄합재대규모수거하진행취류분석。
In order to improve the efficiency of clustering algorithm, this paper proposes a model of fast closed tree paralleled algorithm on the platform of dynamic cloud. Aiming at the random allocation strategy of cloud computing platform Hadoop, the paper puts forward CDA-GA to meet the requirements of the minimized consumption cost. Moreover, on the foundation of CDA-GA, it proposes the dynamic cloud platform model. The parallelization of traditional frequency closed tree mining algorithm and clustering algorithm and is applied in the dynamic cloud platform, this paper designs a closed tree clustering algorithm framework. Experimental results show that the algorithm is feasible and fits into clustering analysis under massive amounts of data.