Unsupervised graph clustering for community detection in complex networks using spectral analysis Online publication date:: Thu, 06-May-2021
by Zakariyaa Ait El Mouden; Abdeslam Jakimi; Moha Hajar
International Journal of Multimedia Intelligence and Security (IJMIS), Vol. 3, No. 4, 2020
Abstract: Clustering is a recent technique for a smart classification of data, where the output is a set of clusters and each cluster regroups data points having similar behaviours. Traditional clustering algorithms are those where we predefine the number of clusters as an input parameter, and we control the size of the neighbourhood, also called supervised clustering. Recently, with data evolution in term of volume and variety, supervised clustering techniques were overwhelmed and unsupervised algorithms started to appear. Spectral clustering (SC) is a graph clustering technique based on spectral analysis, and it is one of the most powerful unsupervised clustering techniques. This paper presents an application of a spectral clustering algorithm to data modelled by graphs and a comparison between the two families of SC; unnormalised SC and normalised SC. We also introduce a modification of normalised SC algorithm to make the number of clusters estimated and not given as an input parameter. Further works are needed to apply the approach to larger datasets in order to evaluate its performance against big data challenges.
Online publication date:: Thu, 06-May-2021
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Multimedia Intelligence and Security (IJMIS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com