Graph Sensitive Indices for Comparing Clusterings

Experiments on Comparing Graph Clusterings

A promising approach to compare graph clusterings is based on using measurements for calculating the distance. Existing measures either use the structure of clusterings or quality–based aspects. Each approach suffers from critical drawbacks. We introduce a new approach combining both aspects and leading to better results for comparing graph clusterings. An experimental evaluation of existing an...

Engineering Comparators for Graph Clusterings

A promising approach to compare two graph clusterings is based on using measurements for calculating the distance between them. Existing measures either use the structure of clusterings or quality-based aspects with respect to some index evaluating both clusterings. Each approach suffers from conceptional drawbacks. We introduce a new approach combining both aspects and leading to better result...

Exploring and Comparing Clusterings of Multivariate Data Sets Using Persistent Homology

Structure-Aware Distance Measures for Comparing Clusterings in Graphs

Clustering in graphs aims to group vertices with similar patterns of connections. Applications include discovering communities and latent structures in graphs. Many algorithms have been proposed to find graph clusterings, but an open problem is the need for suitable comparison measures to quantitatively validate these algorithms, performing consensus clustering and to track evolving (graph) clu...

A Split-Merge Framework for Comparing Clusterings

Clustering evaluation measures are frequently used to evaluate the performance of algorithms. However, most measures are not properly normalized and ignore some information in the inherent structure of clusterings. We model the relation between two clusterings as a bipartite graph and propose a general component-based decomposition formula based on the components of the graph. Most existing mea...