Deep clustering approaches and representation learning-based clustering techniques have gained popularity over traditional clustering methods in the last few years and have shown remarkable performance in unsupervised representation learning [12]. Clustering is a fundamental unsupervised learning approach used in knowledge discovery and data science. Its principal purpose is to group data points based on their main attributes [13] without access to the ground truth labels or prior knowledge of the data distributions, categories, or the nature of the clusters. It is also defined as the unsupervised classification of patterns (observations, data items, or feature vectors) [14]. This means that the established groups could be much more than the required categories, as they can capture even the simplest variances within each class. On the other hand, supervised learning aims to find the correlation between the input and the output label by constructing a predictive model learning from a vast number of training examples, where each training example has a label indicating its ground truth output [15]. Many researchers have published state-of-the-art deep learning neural networks with trained weights [16; 17; 18; 19; 20; 21] on benchmarks datasets such as the ImageNet dataset, which contains a thousands of classes. We intend to use both techniques to join their advantages in a kind of semi-supervised learning [22]. Semi-supervised learning uses large amounts of unlabeled data and a small portion of labeled data to build better classifiers, meaning that it requires less human effort and gives higher accuracy [23] than unsupervised learning approaches. Therefore, we can attempt to find the minimal amount of labeled data that still produces high accuracy. In our implementation, we do not attempt to train any network, as it would require large computational efforts and special knowledge that the domain experts do not possess. Therefore, we will try the second approach by directly employing the feature space and using it in cluster construction to take advantage of the available pre-trained image classification convolutional neural networks. We train a Uniform Manifold Approximation and Projection (UMAP) model [24] to reduce the dimensionality of the problem, effectively establishing the clusters between these images based on their feature space. New implementations tend to treat the clustering and the dimensionality reduction tasks jointly to learn what is known as cluster-friendly representations. These representations have shown that optimizing the two methods together improves the performance of both [25]. Deep clustering techniques are powerful, but they usually suffer from several challenges. For instance, setting the optimal hyper-parameters for these techniques is not a trivial task [26] as the recommended parameters are mainly benchmark-based on datasets such as MNIST [27], and CIFAR-10 [28] that do not reflect the complexity of real-world tasks. Whereas in our adaptation, the network is already trained and fine-tuned by a professional group of deep learning specialists. Nevertheless, we still have some parameters to optimize concerning the dimensionality reduction algorithm, such as the number of neighbors and iterations used in UMAP. Fortunately, these parameters are reasonably limited and easy to understand. In addition, running the algorithm requires a modest amount of resources only. Reducing the size of the data to be processed is a simple way to overcome this limitation. A small subset may be sufficient for fitting the model to obtain a reasonable performance, such as in our case, but careful selection is required to balance the data. The other major issue is the lack of theoretical background and interpretability [26], as explaining why and how these algorithms produce good results. Selecting the right pre-trained network for each dataset is also a challenge. It is often challenging to predict their behavior and performance for out-of-sample cases.