Title
Robust Graph-Theoretic Clustering Approaches Using Node-Based Resilience Measures
Abstract
This paper examines a schema for graph-theoretic clustering using node-based resilience measures. Node-based resilience measures optimize an objective based on a critical set of nodes whose removal causes some severity of disconnection in the network. Beyond presenting a general framework for the usage of node based resilience measures for variations of clustering problems, we emphasize the unique potential of such methods to accomplish the following properties: (i) clustering a graph in one step without knowing the number of clusters a priori, and (ii) removing noise from noisy data. We first present results of clustering experiments using a β-parametrized generalization of vertex attack tolerance, showing high clustering accuracy for both real datasets and equal density synthetic data sets, as well as successful removal of noise nodes. It is shown that arbitrarily increasing β increases the number of noise nodes removed in some cases, and that internal validation measures can be used to determine the correct number of clusters in a class of datasets. Further results are presented using five different resilience measures with a general node-based resilience clustering technique. In a subset of cases a resilience measure, such as integrity, is able to cluster to high accuracy in one step, giving the correct clustering while also determining the correct number of clusters. Integrity is also shown to be promising with respect to noise removal, removing up to 80% of noise on some datasets.
Year
DOI
Venue
2016
10.1109/ICDM.2016.0043
2016 IEEE 16th International Conference on Data Mining (ICDM)
Keywords
Field
DocType
graph theory,clustering,resilience measures,noise
Data mining,Fuzzy clustering,Canopy clustering algorithm,Clustering high-dimensional data,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Computer science,Constrained clustering,Artificial intelligence,Cluster analysis,Machine learning
Conference
ISSN
ISBN
Citations 
1550-4786
978-1-5090-5474-9
0
PageRank 
References 
Authors
0.34
14
5
Name
Order
Citations
PageRank
John Matta1135.94
Tayo Obafemi-Ajayi203.72
Jeffrey Borwey341.53
Wunsch II Donald C.4135491.73
Gunes Ercal517720.05