WedPM1-5
Topological and Graph Based Clustering Methods 1
Chair: Rushed Kanawati, Nistor Grozavu
Room: Conference Room IV

14:00

Tutorial
Topological and Graph Based Clustering: Recent Algorithmic- 15:00 Advances
Rushed Kanawati1 , Nistor Grozavu1
1
A3-LIPN, University Sorbonne Paris Cité

15:00

Parcellating whole brain for individuals by simple linear iterative clustering
121- 15:20 Jing Wang , Zilan Hu , Haixian Wang
12
Southeast University School of Mathematics and Physics,
Anhui University of Technology, Maanshan, Anhui 243002,
PR China

15:20

Overlapping Community Structure Detection of Brain Functional Network Using Non-negative Matrix Factorization
121- 15:40 Xuan Li , Zilan Hu , Haixian Wang
12Southeast University School of Mathematics and Physics,
Anhui University of Technology, Maanshan, Anhui 243002,
PR China

149

WedPM2-5
Topological and Graph Based Clustering Methods 2
Chair: Rushed Kanawati, Nistor Grozavu
Room: Conference Room IV

16:00

Collaborative-based multi-scale clustering in very high resolu- 16:20 tion satellite Images
Jeremie Sublime1 Antoine Cornuéjols1 Younes Bennani21AgroParisTech 2 University Paris 13

16:20

Towards Ontology Reasoning for Topological Cluster Labeling- 16:40 Hatim Chahdi1 , Nistor Grozavu2
1
UMR Espace-Dev & LIPN 2 LIPN, Paris 13 University

16:40

Overlapping community detection using core label propagation and belonging function- 17:00 Jean-Philippe Attal1 , Maria Malek1 , Marc Zolghadri2
1
EISTI : École internationale des sciences du traitement delı́nformation 2 SUPMECA

17:00

A new clustering algorithm for dynamic data
- 17:20 Parisa Rastin1 , Tong Zhang1 , Guenael Cabanes11Université Paris 13153

WedPM1-5

Topological and Graph Based Clustering Methods 1Tutorial
Topological and Graph Based Clustering: Recent Algorithmic Advances
Rushed Kanawati1 , Nistor Grozavu1
1A3-LIPN, University Sorbonne Paris Cité
Abstract
One of the main tasks in the field of high dimensional data analysis and exploration
is to compute simplified, usually visual, views of processed data. Clustering andprojection are two main methods classically applied to achieve this kind of tasks.
Clustering algorithms produce a grouping of data according to a given criterion suchthat similar data items are grouped together. Projection methods represent datapoints in a low-dimensional space such that clusters and the metric relationships ofthe data items are preserved as faithfully as possible. However, in the actual eraof big data and connected devices, a lot of datasets are shaped in form of largescale dynamic attributed graphs. Non relational data can also be shaped in form ofgraphs by applying similarity-based graph construction approaches. New approaches
for data clustering and projection are then required. In this tutorial we make thepoint on the latest algorithmic advances in the field of graph-based and topologicalclustering approaches.
Tutorial outline :1- Introduction
2- Building graphs from data
3- Community detection in complex networks
4- Topological clustering approaches
4.1 Spectral clustering methods
4.2 Self-Organization map
5- Multi-view clustering approaches
5.1 Community detection in multiplex networks
5.2. Collaborative clustering
6- Applications
6.1 A multiplex-based clustering ensemble selection approach.
6.2 Visual information retrieval
7. Conclusion & hot research directions

181