## 4 The kClusterHub Application
### Web Interface  
To use kClusterHub, users must first register and confirm their email. Then, they can log in. The main page is divided into three distinct sections. Through the first section, registered users are able to seamlessly upload their private or public datasets in csv, xls, or xlsx format (see Figure 6). All registered users can select an uploaded dataset among the available private and public datasets (see Figure 7) and, of course, can download or delete it (assuming that the user has the necessary permissions). Then, kClusterHub presents the selected dataset in tabular form (see Figure 8). Also, the names of the dataset's attributes are displayed in check boxes, and this feature enables users to select the attributes that will be the basis for their clustering analysis (see Figure 9). The default mode is to consider all the appropriate attributes. However, the users can uncheck the attributes they want to be ignored during the clustering process. The application offers the k-means, k-modes and k-prototypes clustering for numerical, categorical and mixed data, respectively. Through the first section of the interface, the registered users have the option to either manually select their desired algorithm or opt for the auto mode. The auto mode harnesses the power of automation to determine the most suitable algorithm for the dataset selected. When the dataset comprises only numerical data, k-means with the k-means++ initialization is automatically chosen. Conversely, if only categorical data exist, k-modes is used. K-prototypes is adopted when the dataset has both categorical and mixed data. In cases of k-modes and k-prototypes, the Cao method for centroid initialization is used. On the other hand, if a user selects the desired algorithm (see Figure 10), kClusterHub presents the attributes that can be used, and the user selects among them (see Figure 9). For instance, if the selected dataset has mixed attributes and k-means is selected, the numerical attributes are presented for consideration. Similarly, opting for k-modes exclusively showcases categorical attributes. In essence, ClusterHub's auto mode offers an effortless approach, while the manual selection of an algorithm affords users the control to shape their analysis based on the available attributes. Subsequently, kClusterHub orchestrates the partition-based clustering process using the appropriate centroid initialization method. The second section of the interface concerns the elbow method. The user may use the elbow method for finding the optimal number of clusters (see Figure 11) by entering the maximum number of clusters, \(max\). The application utilizes the selected clustering algorithm \(max\) times and estimates the clustering metric (WOSS, Hamming or mixed distance) for each time. Then, by clicking \"Get elbow chart\", kClusterHub presents the elbow diagram which portrays the trade-off between the number of clusters and the clustering metric aiding users in pinpointing the optimal number of clusters. In addition, kClusterHub, based on the elbow graph, suggests the appropriate number of clusters in the range from 2 to \(max\) value provided. Figure 5: Registered users hierarchy. Figure 8: Selected dataset in the tabular form. Figure 6: Dataset upload screen. Figure 7: Dataset selection. The last section of the interface requires users to input their desired number of clusters. This can be either the number suggested from the elbow method or chosen independently by the user. Upon clicking the \"Get cluster assignment\" button, the selected clustering algorithm runs and a table showcasing the data with the corresponding cluster assignments is presented (see Figure 12). For a visual exploration of the cluster characteristics, users can also opt for the exploratory data plots (see Figure 13). Moreover, users have the option to download the table in CSV format, using the \"Download CSV\" button. Both the elbow graph and the exploratory data plot are available for download as well.