Unsupervised clustering analysis: a multiscale complex networks approach

Abstract

Unsupervised clustering, also known as natural clustering, stands for the classification of data according to their similarities. Here we study this problem from the perspective of complex networks. Mapping the description of data similarities to graphs, we propose to extend two multiresolution modularity based algorithms to the finding of modules (clusters) in general data sets producing a multiscales' solution. We show the performance of these reported algorithms to the classification of a standard benchmark of data clustering and compare their performance.

References in corpus (8)

• Modularity and community structure in networks
• Resolution limit in community detection
• Comparing community structure identification
• Community detection in networks with positive and negative links
• Analysis of the structure of complex networks at different resolution levels
• Analysis of community structure in networks of correlated data
• Solving non-uniqueness in agglomerative hierarchical clustering using multidendrograms
• Natural clustering: the modularity approach

Cited by in corpus (3)

• Hierarchical multiresolution method to overcome the resolution limit in complex networks
• Hierarchical mutual information for the comparison of hierarchical community structures in complex networks
• Mining and Analyzing the Italian Parliament: Party Structure and Evolution