## 2 Related Work  
The development and usage of AutoML tools is one of the machine learning and data mining research fields that has attracted attention during the last years. However, they primarily cater to supervised learning tasks, such as classification and regression, rather than clustering. For instance, auto-sklearn [13] is an AutoML tool for Python that focuses on classification and regression tasks. It does not directly support clustering algorithms like k-means, k-modes, or k-prototypes. H2O.ai [14] provides an AutoML platform that supports various machine learning tasks. While it primarily emphasizes supervised learning, it does offer clustering capabilities. On the other hand, there are many specialized standalone machine learning software tools and libraries of programming languages that support clustering tasks, partition-based clustering included. However, all of them imply specialized knowledge on these tools and/or programming skills as well as knowledge on the research field of data mining and machine learning. Thus, they cannot be used by practitioners who are non-experts. In addition, the software tools require installation on certain operating systems, and some of them need licenses. Therefore, they are inappropriate for many devices. Moreover, the standalone software tools, libraries of programming languages and web-based platforms (e.g., the non-free Amazon SageMaker Canvas [15]), offer a wide range of machine learning algorithms and extensive capabilities for data preprocessing, modeling, and evaluation. However, they entail a steeper learning curve, especially for non-experts, when compared to the specialized focus of kClusterHub, which prioritizes simplicity, accessibility, and automation in partition-based clustering tasks, enabling users to analyze their data without the need for expertise or extensive setup. For example, WEKA [6; 11] stands as a widely recognized and extensively used software suite. Developed by the University of Waikato, WEKA provides a comprehensive collection of machine learning and data mining algorithms and data preprocessing techniques, making it a versatile solution for various data analysis tasks. Its graphical interface and open-source nature have contributed to its popularity, allowing researchers and practitioners to efficiently explore, preprocess, and model data for classification, clustering, regression, association rules mining and other machine learning tasks. WEKA must be downloaded and installed on Linux, Windows or Mac OS operating systems as long as Java is installed and configured. [PERSON] et al. [7] present Orange, an open-source Python-based framework for data mining and machine learning, which can be used by both researchers and experienced users. Orange empowers users to construct workflows, visualize data, and experiment with various machine learning and data mining techniques (k-means clustering included), while the script-oriented capabilities facilitate the integration of novel methods. Like WEKA, Orange must be downloaded and installed on Linux, Windows or Mac OS operating systems. Also, Orange is built on top of the Python language and relies on Python libraries to provide its functionalities. Therefore, having a Python installation is a prerequisite. Scikit-learn [8], a widely used machine learning library in Python, offers an extensive set of tools for various tasks in data analysis and modeling. Developed on the foundation of NumPy, SciPy, and matplotlib, scikit-learn provides a user-friendly interface for tasks such as classification, regression, clustering, and more. Its straightforward API and comprehensive documentation make it an essential resource for researchers seeking efficient and accessible implementations of diverse algorithms. Utilizing scikit-learn necessitates having Python installed on your system and a certain level of programming skills. Since scikit-learn is a Python-based library, users should have a working Python environment to leverage its capabilities.