Feature ranking is a widely used feature selection method. It uses importance scores to evaluate features and selects those with high scores. Conventional unsupervised feature ranking methods do not consider the information on cluster structures; therefore, these methods may be unable to select the relevant features for clustering analysis. To address this limitation, we propose a feature ranking algorithm based on silhouette decomposition. The proposed algorithm calculates the ensemble importance scores by decomposing the average silhouette widths of random subspaces. By doing so, the contribution of a feature in generating cluster structures can be represented more clearly. Experiments on different benchmark data sets examined the properties of the proposed algorithm and compared it with the existing ensemble-based feature ranking methods. The experiments demonstrated that the proposed algorithm outperformed its existing counterparts.

中文翻译：

---

用于聚类分析的集成特征排序算法

特征排序是一种广泛使用的特征选择方法。它使用重要性得分来评估特征并选择得分高的特征。传统的无监督特征排序方法没有考虑聚类结构的信息；因此，这些方法可能无法选择相关特征进行聚类分析。为了解决这个限制，我们提出了一种基于轮廓分解的特征排序算法。所提出的算法通过分解随机子空间的平均轮廓宽度来计算整体重要性分数。通过这样做，可以更清楚地表示特征在生成集群结构中的贡献。在不同基准数据集上的实验检查了所提出算法的特性，并将其与现有的基于集成的特征排序方法进行了比较。