On the Equivalence Between Community Discovery and Clustering

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.