模式识别(心理学)
k均值聚类
数据挖掘
模糊聚类
特征选择
k-中位数聚类
相似性(几何)
高维数据聚类
相关聚类
质心
降维
星团(航天器)
数学
单连锁聚类
作者
Malong Tan,Shichao Zhang,Lin Wu
标识
DOI:10.1007/s00521-018-3836-z
摘要
The key step of spectral clustering is learning the affinity matrix to measure the similarity among data points. This paper proposes a new spectral clustering method, which uses mutual k nearest neighbor to obtain the affinity matrix by removing the influence of noise. Then, the characteristics of high-dimensional data are self-represented to ensure local important information of data by using affinity matrix in standardized processing. Furthermore, we also use the normalization method to further improve the performance of clustering. Experimental analysis on eight benchmark data sets showed that our proposed method outperformed the state-of-the-art clustering methods in terms of clustering performance such as cluster accuracy and normalized mutual information.
科研通智能强力驱动
Strongly Powered by AbleSci AI