融合改进FCM与PFS的知识供需匹配  

Knowledge supply and demand matching based on improved FCM and PFS

作　　者：张建华[1] 刘艺琳 郭启迪 杨俊晓 徐佳璐 ZHANG Jian-hua;LIU Yi-lin;GUO Qi-di;YANG Jun-xiao;XU Jia-lu(School of Management Engineering,Zhengzhou University,Zhengzhou 450001,China)

机构地区：[1]郑州大学管理工程学院,河南郑州450001

出　　处：《计算机工程与设计》2023年第1期99-107,共9页Computer Engineering and Design

基　　金：国家社会科学基金项目(19BTQ035)。

摘　　要：为提升知识资源的有效配置,缓解用户“知识迷向”问题,设计一套知识供需匹配方法。依据DBSCAN算法确定FCM算法的聚类数目,增强聚类效果;基于改进FCM进行区域划分实现匹配空间压缩,提升算法效率。在此基础上,构建模糊关联匹配度模型,通过融合Zadeh与PFS模糊算子改进相似度计算,兼顾用户需求与既有知识间的相关度,确定匹配结果。实验分析表明,其在知识匹配有效性方面具有一定的比较优势。To improve the effective allocation of knowledge resources and alleviate the user’s knowledge obsession problem,a set of knowledge supply and demand matching methods was designed.The number of clusters of the FCM algorithm based on the DBSCAN algorithm was determined to enhance the clustering effect.The matching space compression based on the improved FCM was implemented to achieve matching space compression,to improve the efficiency of the algorithm.A fuzzy association matching degree model was built by fusing Zadeh and PFS fuzzy operator,to improve the similarity calculation.The correlation between user’s needs and existing knowledge was considered to determine the matching result.Experimental analysis shows that it has certain comparative advantages in the effectiveness of knowledge matching.

关 键 词：供需匹配 DBSCAN算法 模糊C均值 Zadeh模糊算子 PFS模糊集 相似度 相关度 

分 类 号：TP312[自动化与计算机技术—计算机软件与理论]