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When some 'entities' are related by the 'features' they share they areamenable to a bipartite network representation. Plant-pollinator ecologicalcommunities, co-authorship of scientific papers, customers and purchases, oranswers in a poll, are but a few examples. Analyzing clustering of suchentities in the network is a useful tool with applications in many fields, likeinternet technology, recommender systems, or detection of diseases. Thealgorithms most widely applied to find clusters in bipartite networks arevariants of modularity optimization. Here we provide an hierarchical clusteringalgorithm based on a dissimilarity between entities that quantifies theprobability that the features shared by two entities is due to mere chance. Thealgorithm performance is $O(n^2)$ when applied to a set of n entities, and itsoutcome is a dendrogram exhibiting the connections of those entities. Throughthe introduction of a 'susceptibility' measure we can provide an 'optimal'choice for the clustering as well as quantify its quality. The dendrogramreveals further useful structural information though -- like the existence ofsub-clusters within clusters or of nodes that do not fit in any cluster. Weillustrate the algorithm by applying it first to a set of synthetic networks,and then to a selection of examples. We also illustrate how to transform ouralgorithm into a valid alternative for one-mode networks as well, and show thatit performs at least as well as the standard, modularity-based algorithms --with a higher numerical performance. We provide an implementation of thealgorithm in Python freely accessible from GitHub.

Hierarchical clustering of bipartite data sets based on the statistical significance of coincidences