基于K-means聚类挖掘智能机器人领域技术创新人才

以智能机器人领域为例,借助机器学习的方法挖掘技术创新人才,消除专家分类的主观性。通过专利信息构建技术创新人才评价指标体系,结合主成分分析、K-means聚类,进行技术创新人才有效分类;利用DWPI手工代码挖掘智能机器人领域对应的创新人员及相应的技术团队成员,对于技术创新人才分类有进一步优化空间。K-means聚类改进了传统的识别方法,突破人工统计的局限,可以处理数量级更大的数据,对数据挖掘可以进行及时、准确、直观的分析。

Mining Technological Innovation Talents in Intelligent Robot Field Based on K-means Algorithms

Taking the intelligent robot field as an example,by means of machine learning,the subjectivity of expert classification can be eliminated.The evaluation index system of technological innovation talents is constructed by patent information,and the effective classification of technological innovation talents is carried out by combining principal component analysis and K-means clustering.The corresponding innovation personnel and corresponding technical team members in the field of intelligent robot are mined by DWPI manual code,which has further optimization space for the classification of technological innovation talents.K-means clustering improves the traditional recognition method,breaks through the limitations of artificial statistics.It can deal with larger data of order of magnitude,and can analyze data mining timely,accurately and intuitively.