基于清晰有理数均值的新匹配聚类算法

A Novel Matching Clustering Algorithm Based on Clear Rational Number Mean

通过改进清晰有理数均值的方法,提出一种新匹配聚类算法.首先计算每条数据的清晰有理数均值,然后与匹配项比较,得出聚类结果,解决了人工标注型数据的聚类问题.将该方法在反欺诈网页领域中进行了检测和验证,并与使用同一名称但不同类型数据集的K最近邻算法进行比较,实验结果表明,该方法在反欺诈网页领域中比K最近邻算法更有效,同时也证明了新匹配聚类算法在人工标注型数据上聚类具有合理性.

We proposed a novel matching clustering algorithm by improving the method of clear rational number mean.First,we calculated the clear rational mean of each piece of data,then compared it with the matching item to get the clustering result,and solved the clustering problem of artificial annotation data.This method was tested and verified in the field of anti-fraudulent Web pages,and compared with the results of K-nearest neighbor algorithm using the same name but different types of data sets.Experimental results show that the method is more effective than K-nearest neighbor algorithm in the field of anti-fraudulent Web pages,and it also proves that the novel matching clustering algorithm is reasonable in clustering artificial annotation data.