## 6 Conclusions and Future Work  
In this paper, we introduced kClusterHub, a versatile and user-friendly web-based application designed to streamline the process of partition-based clustering. Our platform bridges the gap between clustering algorithms and the end users by providing an intuitive interface that enables the easy configuration and execution of clustering tasks. By incorporating algorithms, such as k-means, k-prototypes, and k-modes, along with an intelligent auto-algorithm selection feature and efficient centroid initialization methods, such as k-means++ for numerical data and the Cao method for categorical and mixed data, kClusterHub is able to manage various data types and perform effortless partition-based cluster analysis. The integration of the elbow method for optimal number of clusters auto-determination further enhances the system's decision-making capabilities. The presentation of cluster assignments, in both tabular and exploratory plot formats, facilitates insightful data interpretation. Through empirical evaluations, we showcased the platform's efficiency and efficacy across diverse datasets. Moreover, kClusterHub extends its functionality beyond the web interface by offering a REST API. This API empowers developers to seamlessly integrate partition-based clustering tasks into their own applications. Through straightforward API calls, developers can effortlessly submit their dataset, trigger the execution of the elbow method and subsequent partition-based clustering, and retrieve the resulting cluster assignments. This seamless integration allows developers to leverage the capabilities of partition-based clustering within their applications, providing them with a versatile tool for data analysis. By bridging the gap between the kClusterHub platform and external applications, the REST API offers ways for incorporating clustering techniques into a wider array of use cases, from custom data analytics workflows to automated decision-making processes. In addition to assessing the performance of kClusterHub through CPU measurements, the usability of the platform was also evaluated using the SUS questionnaire. The CPU measurements demonstrated the efficiency of kClusterHub's algorithmic implementations. For the usability evaluation, participants, mainly computer science students, were requested to complete the SUS questionnaire. The results revealed a high SUS score, indicating excellent user satisfaction with kClusterHub's web interface and functionality. These findings underscore the effective performance and user-friendly nature of kClusterHub, making it a valuable tool for partition-based cluster analysis. Our future directions are centered around two key aspects. The first is the integration of density-based clustering (DBSCAN [37]) and hierarchical clustering [38] into kClusterHub. These additions will substantially improve the versatility of the application, enabling users to perform a broader range of data analysis tasks. Also, we plan to optimize the platform's performance by implementing parallelization techniques. By harnessing the power of parallel processing, we aim to significantly expedite the clustering process, particularly for larger datasets. Through these extensions, we aspire to solidify kClusterHub as a complete, efficient tool for effortless data clustering. Conceptualization, K.G., S.O. and D.M. ; methodology, K.G., S.O. and D.M. ; software, K.G. and S.O. ; validation, K.G., S.O. and D.M. ; formal analysis, K.G., S.O. and D.M. ; investigation, K.G., S.O. and D.M. ; resources, K.G., S.O. and G.E. ; data curation, K.G. ; writing--original draft preparation, K.G., S.O. and D.M. ; writing--review and editing, K.G., S.O. and D.M. ; visualization, K.G., S.O. and D.M. ; supervision, S.O. and D.M. ; project administration, K.G., S.O. and D.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Data Availability Statement:** Publicly available datasets were analyzed in this study.