基于约束条件的模糊等价关系聚类方法  

Fuzzy Equivalence Relation Clustering Method Based on Constraint Conditions

作　　者：梁元[1] 车明[1] 

机构地区：[1]天津大学计算机科学与技术学院天津市先进网络重点实验室,天津300350

出　　处：《计算机工程》2017年第6期276-280,288,共6页Computer Engineering

基　　金：国家科技支撑计划项目(2012BAJ24B04)

摘　　要：传统的模糊等价关系聚类方法不能根据具体的约束条件进行聚类,使得聚类结果准确性低,不满足要求。为解决该问题,在传统方法的基础上,根据距离约束条件预处理数据集并且扩维,提出一种新的模糊聚类方法。通过数据间的Euclid距离以及约束条件为每个数据建立数据间关系,用来描述数据间的约束条件满足情况,同时将此作为数据的新增维度,更新原数据集并重新构建相似程度方程,获得对应的相似矩阵并基于模糊等价关系进行聚类。在真实数据集上的实验结果表明,与传统无指导的模糊等价关系聚类方法相比,提出的聚类方法克服了不能根据具体约束条件进行准确聚类的缺陷,具有更高的准确性。The traditional fuzzy equivalence relation clustering method cannot clusteraccording tospecific constraints, so that the clustering results have low accuracy, anddonot meet the requirement. In order to solve this problem, based on traditional fuzzy equivalence relation clustering method, by means of distance constraints for preprocessing and expanding the data set, this paper proposes a novel fuzzy clustering method. According to the Euclid distance and constraint conditions, the inter - data relationship for each data is established, so as to describe whether the distance between data points meet the constraint conditions. These relationships are regarded as the additional dimension. The original data set is updated and the similarity equation is reconstructed to obtain the corresponding similarity matrix and cluster based on fuzzy equivalence relation. Experimental results on real dataset show that compared with the traditional unsupervised fuzzy equivalence relation clustering method, the proposed method overcomes the defectthat it is unable to accurately clusteraccording tospecific constraints, and the clustering results can bettersatisfy the constraint conditionsand they have higher accuracy.

关 键 词：模糊聚类 等价关系 约束条件 增维 准确性 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]