Deep Clustering Using the Soft Silhouette Score: Towards Compact and Well-Separated Clusters

Unsupervised learning has become increasingly important due to the rise of big data collection and the high cost associated with acquiring labeled data. This field of research encompasses various techniques, some of which include generative models [1], representation learning, dimensionality reduction [2] and clustering [3]. Such methods enable us to extract meaningful insight on properties of the data, without relying on explicit guidance or supervision from pre-existing labels. Clustering is a fundamental unsupervised learning task with numerous applications in computer science and many other scientific fields [4-6]. Even though a strict definition of clustering may be challenging to establish, a more flexible interpretation can be stated as follows: Clustering is the process of partitioning a set of objects into groups, known as clusters, such that data in the same group share "common" characteristics while "differing" from data in other groups. While the above clustering definition is simple, it is proven to be a hard machine learning problem [7]. More specifically, it is known that its difficulty arises from several factors like data prepossessing and representation, clustering criterion, optimization algorithm and parameter initialization. Due to its particular importance, clustering is a well-studied problem with numerous proposed approaches. Generally, they can be classified as hierarchical (divisive or agglomerative), model-based (e.g.