Bipartite graph partitioning and spatial bootstrapping methods: A case study of innovative startups

Abstract

Innovative startups are the source of innovation and technological development; therefore, understanding their behavior can help better recognize the business organization's direction. This paper introduces a new method for clustering innovative startups using bipartite graph partitioning combined with spatial bootstrapping, improving clusters' accuracy and interpretability. Recent advancements in clustering techniques have introduced ensemble or consensus clustering methods, which aim to merge multiple clustering results into a superior outcome. A key challenge in this field is effectively integrating diverse clusters, and one promising solution involves utilizing graph formalism and partitioning strategies. By leveraging advanced graph partitioning techniques, we transform the task of partitioning the ensemble graph into a community detection problem. Our methodological approach improves the traditional method of bipartite graphs used in cluster ensembles by implementing the state of the art biLouvain algorithm. We also focused on techniques that could be used to increase the interpretability of the clusters themselves and how they can be used to obtain insightful information from the data. The proposed methodology was applied to a dataset of technologically advanced new businesses, located in the Lombardy region and recorded as innovative startups in the special section of the Italian Chambers of Commerce's Business Register.