A roadmap of clustering algorithms: finding a match for a biomedical application

Concepts

ComputingBioinformaticsBiomedical InformaticsBiomedical Text MiningComputational BiologyData MiningBioinformatics For MedicineCell ClusteringCluster DetectionSpatial ClusteringSpatial PartitioningText ClusteringBiomedical ApplicationsClusteringClustering AlgorithmsGene ClusteringHierarchical ClusteringK-means ClusteringSpatial Clustering Features

TL;DR

Clustering is widely used in bioinformatics, with hierarchical clustering and k‑means being most common, and numerous improvements and alternative approaches such as grid‑based, density‑based, and model‑based methods have been developed. The study aims to match clustering methods to the specific requirements of biomedical applications to improve bioinformatics analysis. The authors review 40 clustering algorithms across all approaches and data types, and introduce a set of desirable features that serve as evaluation criteria. They compare the algorithms using these features, summarizing each method’s benefits and drawbacks to guide selection for biomedical applications.

Abstract

Clustering is ubiquitously applied in bioinformatics with hierarchical clustering and k-means partitioning being the most popular methods. Numerous improvements of these two clustering methods have been introduced, as well as completely different approaches such as grid-based, density-based and model-based clustering. For improved bioinformatics analysis of data, it is important to match clusterings to the requirements of a biomedical application. In this article, we present a set of desirable clustering features that are used as evaluation criteria for clustering algorithms. We review 40 different clustering algorithms of all approaches and datatypes. We compare algorithms on the basis of desirable clustering features, and outline algorithms' benefits and drawbacks as a basis for matching them to biomedical applications.