A complex networks approach for data clustering
This work proposes a method for data clustering based on complex networks theory. A data set is represented as a network by considering different metrics to establish the connection between each pair of objects. The clusters are obtained by taking into account five community detection algorithms. The network-based clustering approach is applied in two real-world databases and two sets of artificially generated data. The obtained results suggest that the exponential of the Minkowski distance is the most suitable metric to quantify the similarities between pairs of objects. In addition, the community identification method based on the greedy optimization provides the best cluster solution. We compare the network-based clustering approach with some traditional clustering algorithms and verify that it provides the lowest classification error rate.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 391 (2012)
Issue (Month): 23 ()
|Contact details of provider:|| Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- N. Lesca, 2010. "Introduction," Post-Print halshs-00640602, HAL.
- Getz, G. & Levine, E. & Domany, E. & Zhang, M.Q., 2000. "Super-paramagnetic clustering of yeast gene expression profiles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 279(1), pages 457-464.
- Domany, Eytan, 1999. "Superparamagnetic clustering of data — The definitive solution of an ill-posed problem," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 263(1), pages 158-169.
- J. M. Kumpula & J. Saramäki & K. Kaski & J. Kertész, 2007. "Limited resolution in complex network community detection with Potts model approach," The European Physical Journal B - Condensed Matter and Complex Systems, Springer, vol. 56(1), pages 41-45, 03.
When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:391:y:2012:i:23:p:6174-6183. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.