Evolving Restricted Boltzmann Machine-Kohonen Network for Online Clustering
This addresses the need for efficient online clustering without prior cluster initialization, offering improvements for applications like real-time data analysis, though it is incremental as it builds on existing methods.
The paper tackles the problem of online clustering for streaming data by proposing ERBM-KNet, which combines an Evolving Restricted Boltzmann Machine with a Kohonen Network to automatically determine cluster numbers and update centers, achieving superior performance on benchmarks and an industry dataset.
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.