未确知聚类在专利质量评价中的应用  

Application of unascertained clustering in patent quality evaluation

作　　者：张妮妮 孙胜娟[1] 张永健[1] ZHANG Nini;SUN Shengjuan;ZHANG Yongjian(School of Information and Electrical Engineering,Hebei University of Engineering,Handan 056001,China)

机构地区：[1]河北工程大学信息与电气工程学院,河北邯郸056001

出　　处：《现代电子技术》2020年第8期143-146,共4页Modern Electronics Technique

基　　金：河北省科技计划项目:服务产业技术创新的专利分析平台建设(17210105D)。

摘　　要：随着人们对知识产权的重视,作为其重要表征的专利的数量呈现爆发式增长,然而专利的质量却没有随之增长。大量的低质量专利不但作用有限,反而会造成社会资源浪费和遏制创新。对于专利质量的评价,目前还没有统一的标准。文中首先对国内外的专利质量指标进行分析,选取出对专利质量影响较大的指标,构建专利质量评价指标模型。同时,以钢铁行业相关专利为目标数据集,分别采用未确知聚类和模糊均值聚类算法对目标专利质量进行分析评价。最终,将目标专利数据聚类出不同的级别,得出高质量专利。在聚类过程中,发现未确知聚类算法在效率和准确率上都有良好的表现。With people′ s attention to intellectual property,the quantity of patents,as an important symbol of intellectual property,has increased explosively,but the quality of patents has not increased with it. A large number of low-quality patents not only play a limited role,but also lead to the waste of social resources and inhibit innovation. Nowadays,there is no unified standard for the evaluation of patent quality. The domestic and foreign patent quality indicators are analyzed,from which the indicators that have a greater impact on the quality of patents are selected to build the patent quality evaluation index model.With the related patents in steel industry as the target data set,the analysis and evaluation of the target patent quality are performed by means of the unascertained clustering algorithm and fuzzy mean clustering algorithm,respectively. The target patent data is clustered into different levels to obtain high-quality patents. During the clustering process,it is found that the unascertained clustering algorithm has good performance in efficiency and accuracy.

关 键 词：专利质量评价 未确知聚类 专利数据分析 评价模型构建 数据集聚类 对比实验 

分 类 号：TN911-34[电子电信—通信与信息系统] TP311.52[电子电信—信息与通信工程]