Contributo in atti di convegno, 2018, ENG, 10.1007/978-3-319-76111-4_34
Guidotti R.; Coscia M.
CNR-ISTI, Pisa, Italy; Harvard University, Cambridge, USA
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.
3rd EAI International Conference on Smart Objects and Technologies for Social Good, pp. 342–352, Pisa, Italy, 29-30/11/2017
Clustering, Community Discovery, Transactional Clustering, Problem Equivalence
ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
ID: 384336
Year: 2018
Type: Contributo in atti di convegno
Creation: 2018-02-21 16:35:07.000
Last update: 2020-06-17 15:41:28.000
CNR authors
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
DOI: 10.1007/978-3-319-76111-4_34
URL: https://link.springer.com/chapter/10.1007/978-3-319-76111-4_34#citeas
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:384336
DOI: 10.1007/978-3-319-76111-4_34
Scopus: 2-s2.0-85043583625