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.

提出了一种新的在线聚类算法，其中一个 Evolving Restricted Boltzmann Machine (ERBM) 嵌入了一种称为 ERBM-KNet 的 Kohonen 网络。该算法通过使用 ERBM 处理无标签图像数据，并利用从 ERBM 提取的特征来预测聚类数和更新簇中心。与其他聚类算法相比，ERBM-KNet 在解决聚类算法常见问题方面表现出显著的改善，并在四个基准数据集和一个行业数据集上进行了广泛的实验评估，证明了其优越性。