A new clustering method based on multipartite networks.

The clustering problem is one of the most studied and challenging in machine learning, as it attempts to identify similarities within data without any prior knowledge. Among modern clustering algorithms, the network-based ones are some of the most popular. Most of them convert the data into a graph in which instances of the data represent the nodes and a similarity measure is used to add edges. This article proposes a novel approach that uses a multipartite network in which layers correspond to attributes of the data and nodes represent intervals for the data. Clusters are intuitively constructed based on the information provided by the paths in the network. Numerical experiments performed on synthetic and real-world benchmarks are used to illustrate the performance of the approach. As a real application, the method is used to group countries based on health, nutrition, and population information from the World Bank database. The results indicate that the proposed method is comparable in performance with some of the state-of-the-art clustering methods, outperforming them for some data sets.