基于改进K最近邻分类算法的不良网页并行识别

互联网中，黄色、暴力、赌博、反动等不良网页大量存在。如果不进行有效过滤，将给搜索服务带来不良的影响。采用改进的K最近邻分类算法来提高识别的准确率，并在虚拟化平台上通过开源的Hadoop软件所提供的MapReduce模型进行分布式并行处理。对比实验结果表明，所采用的识别方法的识别准确率和识别效率都有较大的提高。

Parallel recognition of illegal Web pages based on improved KNN classification algorithm

There are many illegal Web pages on the Internet, which may have pornographic, violent, gambling or reactionary content. Without being filtered effectively, they will exercise a malign influence on the searching services. An improved K-Nearest Neighbors (KNN) classification algorithm to promote the recognition accuracy was proposed and implemented on a virtualized platform following the MapReduce model provided by the open source software Hadoop, which made it distributed and parallel. Through experiments and comparison with the existing work, it is proved that the proposed recognition method improves the accuracy and efficiency greatly.The algorithm is implemented on a virtualized platform following the MapReduce model provided by the open source software Hadoop, which makes it distributed and parallel. Through experiments and comparison with existing work, it is proved that the recognition method we propose improves the accuracy and efficiency greatly.