基于MapReduce的JP算法设计与实现

针对大规模文本聚类分析所面临的海量、高维、稀疏等难题，提出一种基于云计算的海量文本聚类解决方案。选择经典聚类算法Jarvis-Patrick(JP)作为案例，采用云计算平台的MapReduce编程模型对JP聚类算法进行并行化改造，利用搜狗实验室提供的语料库在 Hadoop平台上进行实验验证。实验结果表明，JP算法并行化改造可行，且相对于单节点环境，该算法在处理大规模文本数据时具有更好的时间性能。

Design and Implementation of JP Algorithm Based on MapReduce

This paper analyzes the prevalent problems such as massiveness, high-dimension and sparse of feature vector of the ordinary algori- thms in clustering textual data, then proposes a massive text clustering based on cloud computing technology as a feasible solution. The classical Jarvis-Patrick(JP) algorithm is chosen as a case. It is implemented using MapReduce programming mode and is testified on the cloud computing platform-Hadoop with Sogou corpus provided by Sogou laboratory. Experimental results indicate that the JP algorithm can be paralleled in MapReduce framework and paralled algorithm can handle massive textual data and get a better time performance than single-node environment.