## 2 Related Work  
Recent research activity has demonstrated a plethora of solutions for self-organizing networks as next generation architectures in 3 GPP standards. SON targets in enhancing network performance KPIs such as capacity and quality of service. 3 GPP Release 8 classified SON into three main categories: self-configuration, self-optimization, and self-healing. Machine learning based approaches, both supervised and unsupervised, have been proposed for all three categories of use cases. Some of the most widely used techniques include artificial neural networks for configuration optimization, demand prediction, or resource allocation [12]. Unsupervised techniques that have been tested in the context of SON include self-organized maps, game theory, hidden Markov models, and of course, clustering [13; 14]. Clustering-based solutions appear in various use cases. In [15], a user-centric clustering algorithm was used to minimize the high load on cells. In [16], the K-means algorithm was used to group users to define spatial beams and increase system capacity. In caching applications, clustering is applied to data users to determine the set of the most influential users, the content of which will be cached and re-used in Device-to-Device (D2D) communication [8]. Another class of approaches uses clustering on the 5G cells to define groups with increased mobility and load activity to apply optimization actions in a smaller number of cells such as in [17]. While user-centric unsupervised approaches have been proposed in the context of SON [8; 15; 16], the literature that applies unsupervised techniques in the planning and reconfiguration of 5G networks is not very extensive. Thus, we extend the aforementioned ideas in the context of optimization of the azimuth steering of the 5G gNB cells in mobile networks. This work is the first attempt, to the best of our knowledge, to apply a user-centric clustering-based solution to this aim. Different clustering algorithms have been heavily explored and compared in the literature. K-means is considered the most typical example of partitioning approaches and has been widely used in numerous applications. Its main disadvantage is that it requires the number of clusters K as an input and results in spherical shaped clusters. It also assumes \begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline
3 GPP & 3 rd Generation Partnership Project \\ gNB & Next Generation Node B (NR) \\ eNodeB & Enhanced Node B (LTE) \\ RSRP & Reference Signal Received Power \\ SINR & Signal-to-Inference-plus-Noise Ratio \\ DBSCAN & Density-based Spatial Clustering of Applications with Noise \\ GSM & Global System for Mobile Communications \\ UMTS & Universal Mobile Telecommunications System \\ LTE & Long Term Evolution \\ NR & New Radio \\ KPI & Key Performance Indicator \\ SON & Self-Organizing Network \\ D2D & Device-to-Device \\ MIMO & Multiple-Input and Multiple-Output \\ Lat, Ling & Latitude, Longitude \\ RAN & Radio Access Network \\ OSS & Operations Support Systems \\ DU, UR, SU, RU & Dense Urban, Urban, Sub-Urban, Rural \\ ISD & Inter-Site Distance \\ PCA & Principal Component Analysis \\ UE & User Equipment \\ CDF & Cumulative Distribution Function \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Table of notations used in this paper. that variables have similar variance, which is not difficult to ascertain in the exploratory analysis steps. It usually converges in a smaller number of iterations than most other clustering algorithms, but requires a comparison of the average silhouette coefficient for different numbers of parameter K, which is time consuming [18]. Another class of clustering algorithms that have gained in popularity in the past decade is the density-based approaches. The most indicative algorithm in this class is DBSCAN, which regards clusters as dense areas of data points that are separated by less dense areas. Density-based approaches have a major advantage over partition-based ones, which is the ability to discover clusters of arbitrary shape.