基于相对密度的不确定数据聚类算法

计算机科学 ›› 2015, Vol. 42 ›› Issue (Z11): 72-74.

基于相对密度的不确定数据聚类算法

潘冬明,黄德才

浙江工业大学计算机科学与技术学院杭州310023,浙江工业大学计算机科学与技术学院杭州310023

出版日期:2018-11-14 发布日期:2018-11-14
基金资助:
本文受水利部公益性行业科研专项(201401044)资助

Relative Density-based Clustering Algorithm over Uncertain Data

PAN Dong-ming and HUANG De-cai

Online:2018-11-14 Published:2018-11-14

摘要/Abstract

摘要： 传统的基于相对密度的聚类算法有效地解决了密度聚类算法对参数敏感以及不能区分不同密度等级簇的问题。基于相对密度的不确定聚类算法,借用了相对密度算法的思想,根据不确定数据的特征,定义了不确定数据的距离公式、相对密度、核心点、密度可达等相关概念,从而提出了一种能够有效地处理不确定数据的新算法。数据仿真结果表明了该算法的有效性和可用性。

关键词: 不确定数据,相对密度,聚类

Abstract: Traditional relative density-based clustering algorithm has advantage in handling shortcomings of user-defined parameters’ sensitivity and distinguishing different hierarchy of density.This paper provided a new uncertain data clustering algorithm based on relative density,which defines distance formula,density ratio,core points and density-reachable,and can efficiently handle uncertain data.The simulation results illustrate the validity and availability of the algorithm.

Key words: Uncertain data,Relative density,Clustering

潘冬明,黄德才. 基于相对密度的不确定数据聚类算法[J]. 计算机科学, 2015, 42(Z11): 72-74. https://doi.org/

PAN Dong-ming and HUANG De-cai. Relative Density-based Clustering Algorithm over Uncertain Data[J]. Computer Science, 2015, 42(Z11): 72-74. https://doi.org/

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