摘要
为解决传统聚类算法无法对高维数据聚类的问题,文中提出了一种结合贪心选择和特征加权的TC-Mean shift高维数据聚类算法。通过对一维数据进行聚类,获得一维数据的聚类结果,再通过加权添加维度聚类,最终获得所有维度数据的聚类,实现对高维数据的聚类。测试结果表明,该算法能够准确地对稀疏的高维数据样本进行聚类,能够处理各种维度的数据,具有良好的实际应用价值。
In order to solve the problem that traditional clustering algorithms can not cluster high-dimensional data, a high-dimensional data clustering algorithm combining greedy selection and feature weighting was proposed. By clustering one-dimensional feature data, the clustering results of one-dimensional data were obtained first, and then all dimension data were clustered by adding dimension clustering weights to achieve clustering of high-dimensional data. The results showed that the algorithm can accurately cluster sparse high-dimensional data samples and meet the needs of high-dimensional data clustering processing, and had good practical application value.
作者
向志华
邵亚丽
XIANG Zhihua;SHAO Yali(School of Information Technology,Guangdong Polytechnic College,Zhaoqing 526100,China)
出处
《电子科技》
2019年第11期70-73,共4页
Electronic Science and Technology
基金
广东省教育厅科技项目(201713720010)~~
