Application of Deep Clustering Algorithms

Deep clustering algorithms have gained popularity for clustering complex, large-scale data sets, but getting started is difficult because of numerous decisions regarding architecture, optimizer, and other hyperparameters. Theoretical foundations must be known to obtain meaningful results. At the same time, ease of use is necessary to get used by a broader audience. Therefore, we require a unified framework that allows for easy execution in diverse settings. While this applies to established clustering methods like k-Means and DBSCAN, deep clustering algorithms lack a standard structure, resulting in significant programming overhead. This complicates empirical evaluations, which are essential in both scientific and practical applications. We present a solution to this problem by providing a theoretical background on deep clustering as well as practical implementation techniques and a unified structure with predefined neural networks. For the latter, we use the Python package ClustPy. The aim is to share best practices and facilitate community participation in deep clustering research.