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

作者：B. Andreopoulos, Aijun An, Xiaogang Wang, Michael Schroeder · 发表于：Briefings in Bioinformatics · 年份：2008 · DOI：10.1093/bib/bbn058 · 被引用次数：219 · 研究领域：Gene expression and cancer classification、Bioinformatics and Genomic Networks、Advanced Clustering Algorithms Research

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.