FU-PCM is an effective clustering mode by inducing the regularization constraint in C-means to avoid the interference of noises and outliers. However, it is difficult to obtain the satisfactory performance in the context of imbalance category distribution. To address above problem, first, we propose a novel clustering model, which exploits Pearson Correlation Coefficient to auto-balance the optimal equation according to the category distribution. Then, we extend it to the incremental model, which only learns samples at the current frame to update the original model by mapping centers of two adjacent frames to the distinguishable space, and mining hard centers to recognize new categories in online datasets. In this article, offline and online methods are verified in popular datasets, and experimental results demonstrate the effectiveness and efficiency of the proposed models.

中文翻译：

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具有硬中心的增量聚类

FU-PCM通过在C均值中引入正则约束来避免噪声和离群值的干扰，是一种有效的聚类模式。但是，在不平衡类别分布的情况下很难获得令人满意的性能。为了解决上述问题，首先，我们提出了一种新的聚类模型，该模型利用Pearson相关系数根据类别分布自动平衡最优方程。然后，我们将其扩展到增量模型，该模型仅通过将两个相邻帧的中心映射到可区分的空间，并挖掘硬中心以识别在线数据集中的新类别，从而在当前帧上学习样本以更新原始模型。本文通过流行的数据集中验证了离线和在线方法，