A novel clustering algorithm for attributed graphs based on K-medoid algorithm

Articulateness and plasticity are two essential attributes that make a graph as an efficient model to real life problems. Nowadays, the attributed graph is received lots of attentions because of usability and effectiveness. In this study, a novel k-Medoid based clustering algorithm, which focuses si...

Full description

Saved in:
Bibliographic Details
Published in:Journal of experimental & theoretical artificial intelligence 2018-11, Vol.30 (6), p.795-809
Main Authors: Farzi, Saeed, Kianian, Sahar
Format: Article
Language:English
Subjects:
Algorithms
Attributed graph
Clustering
clustering algorithm
community detection
signal similarity
State of the art
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Articulateness and plasticity are two essential attributes that make a graph as an efficient model to real life problems. Nowadays, the attributed graph is received lots of attentions because of usability and effectiveness. In this study, a novel k-Medoid based clustering algorithm, which focuses simultaneously on both structural and contextual aspects using Signal and the weighted Jaccard similarities, are introduced. Two real life data-sets, Political Blogs and DBLP bibliography, are employed in order to evaluate and compare the proposed algorithm with state-of-the-art clustering algorithms. The results show the superiorities of the proposed algorithm in terms of cluster quality metrics.
ISSN:0952-813X
1362-3079
DOI:10.1080/0952813X.2018.1467498