语义减法聚类研究

计算机工程与科学

语义减法聚类研究

马慧1,赵捧未1,王婷婷2

(1.西安电子科技大学经济与管理学院,陕西西安 710126;
2.鲁东大学信息与电气工程学院,山东烟台 264000）

收稿日期:2015-05-25 修回日期:2015-09-30 出版日期:2016-09-25 发布日期:2016-09-25

A semantic driven subtractive clustering method

MA Hui1，ZHAO Peng-wei1，WANG Ting-ting2

（1.School of Economics and Management,Xidian University,Xi’an 710071;
2.School of Information and Electrical Engineering,Ludong University,Yantai 264000,China）

Received:2015-05-25 Revised:2015-09-30 Online:2016-09-25 Published:2016-09-25

摘要/Abstract

摘要：

针对传统减法聚类算法需要人工输入参数τ1和τ2的不足，对算法进行改进。引入AFS理论，通过隶属度矩阵自动确定密度半径τ1、半自动确定权重参数τ2，提出了改进的语义减法聚类算法SDSCM，并在Iris和Wine数据集上将其与FCM、KMEANS算法进行比较实验。实验结果表明，SDSCM在评价指标语义强度期望上高于FCM、KMEANS 1%~5%。SDSCM的SPT指标低于FCM、KMEANS，算法的类间分离度有待提高。SDSCM较好地解决了传统减法聚类人工输入参数τ1和τ2带来的弊端，并给出了更贴近用户给定语义的聚类。

关键词: 减法聚类, AFS理论, 隶属度矩阵, 语义强度期望

Abstract:

Aiming at the unnecessary need of manual input of parametersτ1 andτ2, we propose a sementic driven subtractive clustering mehtod (SDSCM) to improve the traditional subtractive clustering algorithms by introducing the axiomatic fuzzy sets (AFS) thoery. The density radius τ1 is automatically determined and weight τ2 is semi-automatically determined based on the membership matrix. We compare the SDSCM with the FCM and the KMEANS on the Iris and Wine data sets. Experimental results show that the SDSCM is 1% to 5% higher than the FCM and the KMEANS in terms of evaluation index (semantic strength expectation), while the SPT is lower than the other two methods, where it still needs to be improved. The SDSCM can effectively solve the disadvantages caused by manual input of parameters τ1 and τ2, and find the clusters which are closer to the user semantics.

Key words: subtractive clustering, AFS, membership matrix, semantic strength expectation

马慧1,赵捧未1,王婷婷2. 语义减法聚类研究[J]. 计算机工程与科学.

MA Hui1，ZHAO Peng-wei1，WANG Ting-ting2. A semantic driven subtractive clustering method [J]. Computer Engineering & Science.