A novel online clustering algorithm is presented where an Evolving Restricted Boltzmann Machine (ERBM) is embedded with a Kohonen Network called ERBM-KNet. The proposed ERBM-KNet efficiently handles streaming data in a single-pass mode using the ERBM, employing a bias-variance strategy for neuron growing and pruning, as well as online clustering based on a cluster update strategy for cluster prediction and cluster center update using KNet. Initially, ERBM evolves its architecture while processing unlabeled image data, effectively disentangling the data distribution in the latent space. Subsequently, the KNet utilizes the feature extracted from ERBM to predict the number of clusters and updates the cluster centers. By overcoming the common challenges associated with clustering algorithms, such as prior initialization of the number of clusters and subpar clustering accuracy, the proposed ERBM-KNet offers significant improvements. Extensive experimental evaluations on four benchmarks and one industry dataset demonstrate the superiority of ERBM-KNet compared to state-of-the-art approaches.

翻译：提出一种新颖的在线聚类算法，其中将演化受限玻尔兹曼机与科霍宁网络相融合，称为ERBM-KNet。所提出的ERBM-KNet利用ERBM以单遍模式高效处理流式数据，采用偏差-方差策略进行神经元生长与剪枝，并基于簇更新策略实现在线聚类，包括使用KNet进行簇预测和簇中心更新。首先，ERBM在处理无标注图像数据时演化其架构，有效解缠潜空间中的数据分布。随后，KNet利用ERBM提取的特征预测簇数量并更新簇中心。通过克服聚类算法常见的挑战（如簇数量的先验初始化及聚类精度欠佳），所提出的ERBM-KNet实现了显著改进。在四个基准数据集和一个工业数据集上的广泛实验评估表明，ERBM-KNet相较于现有最优方法具有优越性。