title: A roadmap of clustering algorithms: finding a match for a biomedical application.
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.
authors.affiliation: Biotechnological Centre, Technische Universität Dresden, Germany. williama@biotec.tu-dresden.de
authors.forename: Bill
authors.initials: B
authors.lastname: Andreopoulos
authors.forename: Aijun
authors.initials: A
authors.lastname: An
authors.forename: Xiaogang
authors.initials: X
authors.lastname: Wang
authors.forename: Michael
authors.initials: M
authors.lastname: Schroeder
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology; D020869:Gene Expression Profiling / Q000379:methods; D016247:Information Storage and Retrieval; D015233:Models, Statistical; D025941:Protein Interaction Mapping