## 1 Introduction
The absence of freely available web applications or services dedicated to partition-based clustering is notable within the authors' awareness. This observation serves as another motivation for the current work. This paper extends the previous work by introducing kClusterHub, a web application that builds upon the development of Web-k-means [10], furthering its capabilities to encompass not only k-means but also k-modes and k-prototypes clustering algorithms. kClusterHub deals with the difficulties when performing partition-based clustering by non-experts. The difficulties are summarized as follows: (i) difficulty of the decision of which partition-based clustering algorithm is appropriate for the different data types, (ii) difficulty of the determination of the number of clusters, and (iii) need of programming skills and/or specialized software to perform partition-based clustering. Therefore, the target users of kClusterHub are researchers and practitioners from diverse domains who may not have expertise in clustering algorithms, programming skills and knowledge on specialized software, yet seek an efficient and accessible tool to derive meaningful insights from their data through partition-based clustering. With kClusterHub, users can seamlessly perform clustering analyses without the need to download, setup and use specialized software (e.g., WEKA [6; 11]) or write code and use programming libraries (e.g., Scikit-learn [8]). The overarching objective of the paper is to create a seamless and user-friendly AutoML-driven environment for non-expert researchers and practitioners seeking to conduct partition-based clustering across numerical, categorical and mixed data. In the spirit of automated machine learning (AutoML) [12], kClusterHub takes a step further in facilitating the clustering tasks. By recognizing the attributes' data types within the uploaded dataset, the tool selects the suitable clustering algorithm from the trio of k-means, k-modes, and k-prototypes. Moreover, kClusterHub is capable of suggesting the optimal number of clusters by utilizing the elbow method. Both aforementioned capabilities are AutoML-inspired. Thus, kClusterHub empowers users to seamlessly upload datasets, select features and execute clustering analyses, via a user-friendly interface. The tool presents the insightful elbow method graph, suggests the optimal number of clusters, and offers a detailed exploratory plot of the cluster assignments that enables users to understand the characteristics of each discovered cluster. In essence, the benefit of using kClusterHub is that it empowers even non-expert users in cluster analysis to effortlessly perform partition-based cluster analyses, making it an useful tool for researchers and practitioners seeking insights from their data. In addition, kClusterHub offers a REST API that programmers can use to access the capabilities of the tool programmatically from their own code. This allows for the integration of kClusterHub's functionalities into other applications. Therefore, kClusterHub not only enhances the accessibility of clustering analyses to non-experts but also aligns with the direction of automated decision making in machine learning. Consequently, kClusterHub deals with the difficulties when performing clustering analysis by non-experts. The difficulties are summarized as follows: (i) difficulty of the decision of which clustering algorithm is appropriate for the different data types, (ii) determination of the optimal k value, and (iii) need of programming skills and/or specialized software. The rest of the paper is organized as follows: Section 2 reviews the related works. Section 3 provides an overview of k-means, k-modes, and k-prototypes as well as the corresponding cluster centroid's initialization methods. The same section reviews the elbow method, which serves as a cornerstone for the optimal number of clusters determination. The kClusterHub app is presented in Section 4.