Binary-Positive下的并行化CURE算法

Parallel CURE algorithm with Binary-Positive

当CURE算法在处理不均匀的海量数据时,针对随机抽样不具有代表性的问题,提出了一种健壮的并行化改进算法。该算法使用Binary-Positive算法得到原始数据的有效属性,并利用MapReduce并行框架对有效数据进行层次聚类,从而实现了正确率与效率的一种权衡。实验分析表明,改进后的CURE算法具有更高的执行效率,且聚类效果良好。

For random sampling is not representative, it proposes a robust parallel improvement of algorithms when using CURE algorithm to handle non-uniform mass data. It uses the Binary-Positive algorithm to get the effective properties of the data, uses valid data for hierarchical clustering with MapReduce, which is a distributed parallel framework. It achieves the correct rate and efficiency of a trade-off. The tests show that the improved CURE algorithm has a higher efficiency in the implementation and has a good clustering result.