Enhance Supervised Self-Organization Clustering by Utilizing Unsupervised Learning Embeddings on Discrete Data

The self-organizing map (SOM) is an unsupervised, competitive learning neural network that projects high-dimensional data onto a low-dimensional grid, effectively showcasing the topological relationships within the original dataset. However, the conventional SOM training algorithm is restricted to numeric data. Categorical data typically needs to be converted into binary format before SOM training, which can lead to the loss of crucial similarity information between categorical values. As a result, the trained SOM may not accurately reflect the true topological order. While a training data splitting method (TDSM) can help identify perfect representative neurons and enhance clustering outcomes, the training data itself often lacks sufficient information, such as data distribution, and can be uncertain and ambiguous. Even when perfect neurons are identified, further improvements in clustering results become challenging. This paper investigates the possibility of improving the performance of supervised TDSM SOM clustering by utilizing unsupervised self-organization granule encoding for discrete data. This approach to unsupervised learning is advantageous for uncovering uncertain and ambiguous information within discrete data, leading to a more effective topological representation of the training data.