Twin Support Vector Clustering (TWSVC) is a clustering algorithm inspired by the principles of Twin Support Vector Machine (TWSVM). TWSVC has already outperformed other traditional plane based clustering algorithms. However, TWSVC uses hinge loss, which maximizes shortest distance between clusters and hence suffers from noise-sensitivity and low re-sampling stability. In this article, we propose Pinball loss Twin Support Vector Clustering (pinTSVC) as a clustering algorithm. The proposed pinTSVC model incorporates the pinball loss function in the plane clustering formulation. Pinball loss function introduces favorable properties such as noise-insensitivity and re-sampling stability. The time complexity of the proposed pinTSVC remains equivalent to that of TWSVC. Extensive numerical experiments on noise-corrupted benchmark UCI and artificial datasets have been provided. Results of the proposed pinTSVC model are compared with TWSVC, Twin Bounded Support Vector Clustering (TBSVC) and Fuzzy c-means clustering (FCM). Detailed and exhaustive comparisons demonstrate the better performance and generalization of the proposed pinTSVC for noise-corrupted datasets. Further experiments and analysis on the performance of the above-mentioned clustering algorithms on structural MRI (sMRI) images taken from the ADNI database, face clustering, and facial expression clustering have been done to demonstrate the effectiveness and feasibility of the proposed pinTSVC model.

孪生支持向量聚类 (TWSVC) 是一种受孪生支持向量机 (TWSVM) 原理启发的聚类算法。TWSVC 已经超越了其他传统的基于平面的聚类算法。然而，TWSVC 使用铰链损失，它最大化集群之间的最短距离，因此受到噪声敏感性和低重采样稳定性的影响。在本文中，我们提出弹球损失双支持向量聚类（pinTSVC）作为聚类算法。提出的 pinTSVC 模型在平面聚类公式中结合了 pinball 损失函数。Pinball 损失函数引入了有利的特性，例如噪声不敏感和重采样稳定性。所提出的 pinTSVC 的时间复杂度仍然与 TWSVC 的时间复杂度相同。已经提供了关于噪声损坏的基准 UCI 和人工数据集的大量数值实验。所提出的 pinTSVC 模型的结果与 TWSVC、双有界支持向量聚类 (TBSVC) 和模糊 c 均值聚类 (FCM) 进行了比较。详细而详尽的比较表明，所提出的 pinTSVC 对于噪声损坏的数据集具有更好的性能和泛化性。对上述聚类算法对取自 ADNI 数据库的结构 MRI (sMRI) 图像、人脸聚类和面部表情聚类的性能进行了进一步的实验和分析，以证明所提出的 pinTSVC 模型的有效性和可行性。双有界支持向量聚类 (TBSVC) 和模糊 c 均值聚类 (FCM)。详细而详尽的比较表明，所提出的 pinTSVC 对于噪声损坏的数据集具有更好的性能和泛化性。对上述聚类算法对取自 ADNI 数据库的结构 MRI (sMRI) 图像、人脸聚类和面部表情聚类的性能进行了进一步的实验和分析，以证明所提出的 pinTSVC 模型的有效性和可行性。双有界支持向量聚类 (TBSVC) 和模糊 c 均值聚类 (FCM)。详细而详尽的比较表明，所提出的 pinTSVC 对于噪声损坏的数据集具有更好的性能和泛化性。对上述聚类算法对取自 ADNI 数据库的结构 MRI (sMRI) 图像、人脸聚类和面部表情聚类的性能进行了进一步的实验和分析，以证明所提出的 pinTSVC 模型的有效性和可行性。