Abstract
Clustering is the subset of data mining techniques used to agnostically classify entities by looking at their attributes. Clustering algorithms specialized to deal with complex networks are called community discovery. Notwithstanding their common objectives, there are crucial assumptions in community discovery – edge sparsity and only one node type, among others – which makes its mapping to clustering non trivial. In this paper, we propose a community discovery to clustering mapping, by focusing on transactional data clustering. We represent a network as a transactional dataset, and we find communities by grouping nodes with common items (neighbors) in their baskets (neighbor lists). By comparing our results with ground truth communities and state of the art community discovery methods, we show that transactional clustering algorithms are a feasible alternative to community discovery, and that a complete mapping of the two problems is possible.
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Notes
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Note that this is far from being an unproblematic definition [11], but it will do for the scope of this paper.
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Acknowledgements
This work is partially supported by the European Project SoBigData: Social Mining & Big Data Ecosystem, http://www.sobigdata.eu, GS501100001809, 654024. Michele Coscia has been partly supported by FNRS, grant #24927961.
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Guidotti, R., Coscia, M. (2018). On the Equivalence Between Community Discovery and Clustering. In: Guidi, B., Ricci, L., Calafate, C., Gaggi, O., Marquez-Barja, J. (eds) Smart Objects and Technologies for Social Good. GOODTECHS 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 233. Springer, Cham. https://doi.org/10.1007/978-3-319-76111-4_34
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