Algorithms for clustering data
Algorithms for clustering data
Elements of information theory
Elements of information theory
Performance criteria for graph clustering and Markov cluster experiments
Performance criteria for graph clustering and Markov cluster experiments
A cluster algorithm for graphs
A cluster algorithm for graphs
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Comparing clusterings: an axiomatic view
ICML '05 Proceedings of the 22nd international conference on Machine learning
Significance-Driven Graph Clustering
AAIM '07 Proceedings of the 3rd international conference on Algorithmic Aspects in Information and Management
Multilevel local search algorithms for modularity clustering
Journal of Experimental Algorithmics (JEA)
Modularity-driven clustering of dynamic graphs
SEA'10 Proceedings of the 9th international conference on Experimental Algorithms
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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 results for comparing graph clusterings. An experimental evaluation of existing and new measures shows that the significant drawbacks of existing techniques are not only theoretical in nature but manifest frequently on different types of graphs. The evaluation also proves that the results of our new measures are highly coherent with intuition, while avoiding the former weaknesses.