中文摘要：

借助评论者的行为特性,提出一种基于评论者行为特征的自适应聚类的虚假评论检测方法.首先,根据评论数据定义自身基本特征以及与其他评论之间的关联性特征,并对每维特征进行归一化处理;其次,根据每一条评论的特征构建聚类矩阵,利用F统计量对K均值算法进行改进,实现评论数据的自适应聚类;最后,计算每个簇偏离整个评论数据集的程度,根据阈值确定异常簇,从而实现虚假评论检测.利用领域评论数据进行实验,结果表明基于自适应聚类的虚假评论检测方法取得了较好的效果.

英文摘要：

With the behavior characteristics of the reviewers,we propose one fake review detection method based on adaptive clustering from the behavior features of reviewers.Firstly,according to the reviewed data,we define the basic features and correlation features with other reviewers,and normalize the features of each dimension.Secondly,we build a clustering matrix based on the features of each review using F statistic to improve the K-means algorithm,and to achieve adaptive clustering for reviews.Finally,we calculate the degree of deviation from the entire review data set for each cluster,and determine abnormal clusters based on the threshold value to achieve fake review detection.Our experimental results show that it gets a better effect to use the method of fake reviews detection based on adaptive clustering using areas ＇review data.