【摘 要】
：
针对大规模数据场景下K-means聚类算法存在总体速度较慢的问题,提出了一种基于大数据流计算框架Apache Flink,并结合流处理思想的SK-means策略.从算法优化方面,采用流处理的思想在数据读入的过程中确定聚类的初始聚类中心;从实现方案上,选用Apache Flink框架设计了一种基于SK-means策略的优化方案,分析该策略对聚类效果和耗时的影响.实验结果表明:该策略在数据量较大的场景中有较好的优化效果,不需要预先假设出聚类数量和初始聚类中心的具体值,有效地降低了聚类迭代次数,降低了整体计算耗
【机 构】
：
北京信息科技大学计算机学院,北京100101;北京信息科技大学计算机学院,北京100101;北京信息科技大学北京材料基因工程高精尖创新中心,北京100101;国家信息中心,北京100038
【出 处】
：
北京信息科技大学学报（自然科学版）
论文部分内容阅读
针对大规模数据场景下K-means聚类算法存在总体速度较慢的问题,提出了一种基于大数据流计算框架Apache Flink,并结合流处理思想的SK-means策略.从算法优化方面,采用流处理的思想在数据读入的过程中确定聚类的初始聚类中心;从实现方案上,选用Apache Flink框架设计了一种基于SK-means策略的优化方案,分析该策略对聚类效果和耗时的影响.实验结果表明:该策略在数据量较大的场景中有较好的优化效果,不需要预先假设出聚类数量和初始聚类中心的具体值,有效地降低了聚类迭代次数,降低了整体计算耗时.
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