当前位置: X-MOL 学术Pattern Recogn. › 论文详情
UNIC: A fast nonparametric clustering
Pattern Recognition ( IF 7.196 ) Pub Date : 2019-11-19 , DOI: 10.1016/j.patcog.2019.107117
Nadiia Leopold; Oliver Rose

Clustering is among the tools for exploring, analyzing, and deriving information from data. In the case of large data sets, the real burden to the application of clustering algorithms can be their complexity and demand of control parameters. We present a new fast nonparametric clustering algorithm, UNIC, to address these challenges. To identify clusters, the algorithm evaluates the distances between selected points and other points in the set. While assessing these distances, it employs methods of robust statistics to identify the cluster borders. The performance of the proposed algorithm is assessed in an experimental study and compared with several existing clustering methods over a variety of benchmark data sets.

更新日期:2020-01-04

 

全部期刊列表>>
物理学研究前沿热点精选期刊推荐
chemistry
自然职位线上招聘会
欢迎报名注册2020量子在线大会
化学领域亟待解决的问题
材料学研究精选新
GIANT
ACS ES&T Engineering
ACS ES&T Water
ACS Publications填问卷
屿渡论文,编辑服务
阿拉丁试剂right
南昌大学
王辉
南方科技大学
彭小水
隐藏1h前已浏览文章
课题组网站
新版X-MOL期刊搜索和高级搜索功能介绍
ACS材料视界
天合科研
x-mol收录
赵延川
李霄羽
廖矿标
朱守非
试剂库存
down
wechat
bug