Unsupervised Clustering Analysis: a Multiscale Complex Networks Approach

@article{Granell2012UnsupervisedCA,
  title={Unsupervised Clustering Analysis: a Multiscale Complex Networks Approach},
  author={Clara Granell and Sergio G{\'o}mez and Alex Arenas},
  journal={Int. J. Bifurc. Chaos},
  year={2012},
  volume={22}
}
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… 

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References

SHOWING 1-10 OF 35 REFERENCES

Data clustering: a review

An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.

Clustering algorithm for determining community structure in large networks.

The results suggest that the proposed algorithm is a good choice to analyze the community structure of medium and large networks in the range of tens and hundreds of thousand vertices.

Analysis of community structure in networks of correlated data.

A reformulation of modularity that allows the analysis of the community structure in networks of correlated data and preserves the probabilistic semantics of the original definition even when the network is directed, weighted, signed, and has self-loops.

Natural clustering: the modularity approach

We show that modularity, a quantity introduced in the study of networked systems, can be generalized and used in the clustering problem as an indicator for the quality of the solution. The

Community detection in graphs

Survey of clustering algorithms

  • R. XuD. Wunsch
  • Computer Science
    IEEE Transactions on Neural Networks
  • 2005
Clustering algorithms for data sets appearing in statistics, computer science, and machine learning are surveyed, and their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts are illustrated.

Detecting fuzzy community structures in complex networks with a Potts model.

A fast community detection algorithm based on a q-state Potts model that allows for the detection of overlapping ("fuzzy") communities and quantifying the association of nodes with multiple communities as well as the robustness of a community.

Cartography of complex networks: modules and universal roles

It is demonstrated that one can (i) find modules in complex networks and (ii) classify nodes into universal roles according to their pattern of within- and between-module connections, which yields a ‘cartographic representation’ of complex networks.

Fast algorithm for detecting community structure in networks.

  • M. Newman
  • Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2004
An algorithm is described which gives excellent results when tested on both computer-generated and real-world networks and is much faster, typically thousands of times faster, than previous algorithms.