电子学报2016，Vol.44Issue(10)：2530-2534,5.DOI:10.3969/j.issn.0372-2112.2016.10.035
基于核距离的直觉模糊c均值聚类算法
Intuitionistic Fuzzy c-means CIustering AIgorithm Based on KerneIIed Distance
摘要
Abstract
The intuitionistic fuzzy c-means clustering algorithm cannot discover the non-convex cluster structure.To alleviate this problem,an intuitionistic fuzzy c-means clustering algorithm based on kernelled distance is proposed.By defi-ning the intuitionistic fuzzy Euclid distance,we map the sample to a high-dimension feature space.So the former features can be reflected thoroughly,which is helpful for clustering.Experiments executed on one artificial data sets and one UCI data sets demonstrate the performance of the proposed method.Compared with the five classical cluster algorithms,our method is of obvious effectiveness and superiority.关键词
直觉模糊集/直觉模糊聚类/核方法/无监督学习Key words
intuitionistic fuzzy set/intuitionistic fuzzy clustering/kernel method/unsupervised learning分类
信息技术与安全科学引用本文复制引用
余晓东,雷英杰,宋亚飞,岳韶华,申晓勇..基于核距离的直觉模糊c均值聚类算法[J].电子学报,2016,44(10):2530-2534,5.基金项目
国家自然科学基金（No．61272011，No．61309022）；陕西省自然科学青年基金 （）
