# Soft Semi-Supervised Deep Learning-Based Clustering
## 1 Introduction  
Supervised machine learning is an extensively studied and applied topic in the artificial intelligence field [1; 2; 3]. This learning paradigm is a powerful tool for data classification using machine language. However, supervised learning requires the availability of accurately labeled datasets to infer learning models [4]. Despite the learning capability of the state-of-the-art supervised machine learning algorithms, the continuously growing size of the datasets used to train the models has made the labeling an even more costly process in terms of time and effort. In contrast, when only a relatively small dataset is available, it typically becomes prone to overfitting the training set. This disadvantage of supervised learning has promoted research on unsupervised machine learning which does not require labeled data for the training stage. In fact, unsupervised machine learning can cope better with a huge amount of unlabeled data as it eliminates the requirement of carefully annotating the training sets. Clustering is an unsupervised learning technique that is meant to draw conclusions and discover the hidden patterns in unlabeled datasets. Clustering has been adapted to address various problems in many areas, such as data mining, pattern recognition, and computer vision [5]. In many fields, there are obvious benefits to be acquired from grouping instances that share similar properties into different clusters in an unsupervised manner [6]. Clustering algorithms have been extensively studied, as a descriptive data mining tool, from various aspects including similarity measures, feature selection, and grouping methods [7]. Among the well-knownand widely used clustering algorithms, one can cite k-means [8], hierarchical clustering [9], and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) [10]. The main concept of DBSCAN is that for a data object to belong to a cluster, the density in a neighborhood for that data object should be high enough, satisfying a user-specified density threshold [11]. This threshold is represented by two hyper-parameters; the radius of the neighborhood (eps) and the minimum number of points required to from the neighborhood (MinPts). Despite DBSCAN algorithm's robustness to outliers and capability of handling irregularly shaped clusters, it deteriorates when dealing with high dimensional data and is sensitive to the input parameters [12]. The typical goal of clustering is to group sets of data objects together in a way that instances assigned to the same cluster are more similar to each other compared to those belonging to other clusters. In fact, grouping similar data entities together is intended to discover the data partition and provide insights on the patterns underlying the different categories. Commonly, for a clustering algorithm to achieve the data mining goal, it should be preceded by a feature extraction stage that is intended to encode and extract the data properties that can better discriminate between the hidden clusters. However, this feature extraction phase may yield a highly dimensional data representation and a curse of dimensionality problem [13]. To meet the curse of dimensionality challenges, several dimensionality reduction methods [14; 15] have been introduced to transform the data from the original feature space into a new feature space with fewer dimensions. However, despite these contributions, the data that exhibit a highly complex latent structure remain challenging to cluster using the existing clustering methods [5]. The emergence and rapid development of deep neural networks (DNNs) has triggered revisiting the clustering-related research. The rationale was to exploit the deep neural networks' ability to automatically determine the most relevant features and relax the need for feature handcrafting and engineering [5; 16].