  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.
