Similarity-based clustering using a network analysis approach. Leandro Fabio Ariza Jiménez
Abstract: Networks represent relations between objects connected pairwise. Networks can have community structure, that is, objects interacting in a network can be organized into groups called communities. In addition, objects forming a community probably share some common properties as well as play similar roles within the interacting phenomenon that is being represented by the network. Thus, community detection can provide an insight into the structure of the networks. Evident interactions between entities are often represented as networks, such as a social network of friendships between individuals or a network of citations between scientific papers. However, networks can be also used to represent similarity relationships between objects. Then, when it comes to cluster objects based on the above criteria, this problem could be solved by means of network community detection algorithms, rather than follow a cluster analysis approach. In this talk we expose an alternative approach for data clustering based on network community detection algorithms. Details about the implementation and performance of this approach are given. In addition, this approach is exemplified by applying it in the identification and delimiting of microbial genomic populations.
Affiliation: PhD student in Mathematical Engineering, GRIMMAT – Research group in mathematical modeling, EAFIT University.
Universidad EAFIT. Mayo 8 de 2017