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. 非監督室的分群法，例如K均值法、階層分群法和模糊C均值法，已經廣為應用在微陣列資料上，目的是找出生物相關的基因群。近期的研究指出，結合生物資訊到驗證方法來評量分群結果的品質，有助益於生物及生醫知識的發掘。在本研究中，我們發展了兩個生物驗證指標，軟生物齊一性指標及軟生物穩定性指標，用來量化糊模分群法的優劣。例如糊模C均值法和以模型為基礎的分群法。我們評量了幾個軟分群法在模擬資料及實際的資料上的表現。結果顯示，軟生物驗證指標可以提供較高的精確度及正確率。同時，我們也討論這些軟生物驗證指標的顯著性。
Relation:
Computational Statistics & Data Analysis 55(5), pp.1969–1979
