Modeling uncertain data using Monte Carlo integration method for clustering
作者：
Highlights：
• Uncertain data is expressed as pds using a modified Monte-Carlo Integration method.
• Three distance metrics have been presented to find the similarity between two pds.
• These metrics have been derived from the notion of KL & Jeffreys divergences.
• Finally, k-medoid & DBSCAN clustering algorithm has been modified for clustering.
摘要
•Uncertain data is expressed as pds using a modified Monte-Carlo Integration method.•Three distance metrics have been presented to find the similarity between two pds.•These metrics have been derived from the notion of KL & Jeffreys divergences.•Finally, k-medoid & DBSCAN clustering algorithm has been modified for clustering.
论文关键词：Uncertain data modeling,Monte Carlo integration,Kullback–Leibler divergence,Jeffreys divergence,Clustering analysis
论文评审过程：Received 22 November 2018, Revised 10 May 2019, Accepted 24 June 2019, Available online 24 June 2019, Version of Record 5 July 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.06.050
