计算机科学 ›› 2011, Vol. 38 ›› Issue (8): 25-28.
• 综述 • 上一篇 下一篇
徐丽,丁世飞
出版日期:
发布日期:
基金资助:
XU Li, DING Shi fei
Online:
Published:
摘要: 信息粒度是对信息和知识细化的不同层次的度量。基于信息粒度的聚类分析方法,凭借能够灵活选择粒度结构,消除聚类结果和先验知识之间的不协调性,有效完成聚类任务等优点,成为国内外学者的研究热点之一。从粗糙集、模糊集、商空间3个理论角度与传统聚类算法相结合,阐述并分析了把粒度的思想引入到聚类中的有效算法及其优缺点,并对这样结合后处理高维复杂数据的可行性及有效性做了分析与展望。
关键词: 信息粒度,粗糙集,模糊集,商空间理论,聚类算法
Abstract: Information granularity is a measure of different levels for refining information and knowledge. With the advantages of selecting granularity structure flexibly, eliminating incompatibility between clustering results and priori knowledge, completing clustering task effectively, granularity clustering methods become one of the focus at home and abroad. In this paper, combined the traditional clustering algorithms from the view of rough set, fuzzy set and ctuotient space theories, effective clustering algorithms with the idea of granularity and their merits and faults were studied and generalized. Finally, the feasibility and effectiveness of handling high-dimensional complex massive data with combina- lion of these theories were forecasted and outlooked.
Key words: Information granularity, Rough set, Fuzzy set, Theory of quotient space, Clustering algorithm
徐丽,丁世飞. 粒度聚类算法研究[J]. 计算机科学, 2011, 38(8): 25-28. https://doi.org/
XU Li, DING Shi fei. Research on Granularity Clustering Algorithms[J]. Computer Science, 2011, 38(8): 25-28. https://doi.org/
0 / / 推荐
导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks
链接本文: https://www.jsjkx.com/CN/
https://www.jsjkx.com/CN/Y2011/V38/I8/25
Cited