Deep Clustering

Clustering is an important unsupervised learning approach for (unlabelled) data mining that partitions the data into groups with similar objects to discover interesting patterns from the dataset. Deep learning-based clustering techniques have recently gained popularity due to their ability to handle complex high-dimensional data. However, these algorithms have limitations, such as the need to specify the number of clusters explicitly beforehand, the lack of information about the inherent cluster structure of data points, and difficulties in interpreting the neural network’s predictions. To overcome these limitations, researchers have used visual assessment of clustering tendency (VAT) methods to estimate the number of clusters present in data. However, these methods are not practical and are often inconclusive with high-dimensional datasets which is the case in most real-life data. Therefore, this project aims to develop an explainable, self-supervised learning-based visual-analytical framework for cluster structure assessment to discover the deep structures present in complex, high-dimensional data when no ground truths are available.