Clustering of gene expression data has been proven to be very useful in various applications, i.e., identifying the natural structure inherent in gene expression, understanding gene functions, mining relevant information from noisy data, and understanding gene regulation. In all these applications, genes, i.e., features, play a crucial role in characterizing them into different groups. These features may be relevant, irrelevant, or redundant, but they have different contributions during the clustering process. This paper presents a novel approach by considering the effect of features during the clustering process. In the proposed method, the fuzzy c{c} -means the objective function is modified using a weighted Euclidean distance between the features with a monotonically decreasing function. The monotonically decreasing function helps control the features’ contribution during the clustering process to partition the data into more relevant clusters. The proposed approach is validated, and performance is presented in various clustering performance measures on the different standard datasets. These clustering performance measures have also been compared with multiple state-of-the-art methods.

中文翻译：

---

使用特征加权鲁棒模糊均值聚类进行基因表达数据分析


基因表达数据的聚类已被证明在各种应用中非常有用，即识别基因表达固有的自然结构、理解基因功能、从噪声数据中挖掘相关信息以及理解基因调控。在所有这些应用中，基因（即特征）在将它们划分为不同的组时发挥着至关重要的作用。这些特征可能是相关的、不相关的或冗余的，但它们在聚类过程中具有不同的贡献。本文通过考虑聚类过程中特征的影响，提出了一种新颖的方法。在所提出的方法中，模糊 c{c} -意味着使用具有单调递减函数的特征之间的加权欧几里得距离来修改目标函数。单调递减函数有助于控制聚类过程中特征的贡献，以将数据划分为更相关的聚类。所提出的方法经过验证，并且在不同标准数据集上的各种聚类性能度量中呈现了性能。这些聚类性能指标还与多种最先进的方法进行了比较。