Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A knowledge-driven approach to cluster validity assessment
Bioinformatics
Separation index and partial membership for clustering
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Spontaneous clustering via minimum gamma-divergence
Neural Computation
Hi-index | 0.03 |
Unsupervised clustering methods such as K-means, hierarchical clustering and fuzzy c-means have been widely applied to the analysis of gene expression data to identify biologically relevant groups of genes. Recent studies have suggested that the incorporation of biological information into validation methods to assess the quality of clustering results might be useful in facilitating biological and biomedical knowledge discoveries. In this study, we generalize two bio-validity indices, the biological homogeneity index and the biological stability index, to quantify the abilities of soft clustering algorithms such as fuzzy c-means and model-based clustering. The results of an evaluation of several existing soft clustering algorithms using simulated and real data sets indicate that the soft versions of the indices provide both better precision and better accuracy than the classical ones. The significance of the proposed indices is also discussed.