## 4 Results
### Jobs Advertisement Requirements Cluster Analysis for Demand Understanding  
In this section, we present the results of our comprehensive analysis of natural language processing (NLP) and clustering-based job profile extraction from a large dataset of over half a million job advertisements. The primary objective of this study was to synthesize meaningful and coherent job profile descriptions by employing advanced clustering techniques and NLP algorithms, thereby facilitating a deeper understanding of the labor market dynamics and the evolving nature of these job requirements. The results of our analysis not only provide valuable insights into the underlying structure and patterns of job profiles, but also highlight the frequency changes of extracted job profiles over time. In doing so, we aimed to contribute to this scientific field and human resource management by offering a data-driven, robust, and scalable approach to identifying the key trends and shifts in the job market. In the visualization presented below (see Figure 4), the automatically extracted job profiles have been depicted, which were generated utilizing clustering techniques and natural language processing methodologies. The results obtained through these methods demonstrated the successful creation of distinct job profiles. A closer examination of the skills identified in the first job profile reveals competencies such as \"accounting\", \"finance\", \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Parameters** & **[PERSON]–[PERSON]** & **[PERSON]–Harabasz** \\ \hline \multirow{4}{*}{K-means} & \{’n\_clusters’: 5\} & 0.9143 & **3386** \\  & \{’n\_clusters’: 10\} & 1.0041 & 2995 \\  & \{’n\_clusters’: 20\} & 1.0487 & 2551 \\ \hline \multirow{4}{*}{DBSCAN} & \{’eps’: 0.2, \textbackslash{min\_samples’: 30\} & 1.1245 & 1352 \\  & \{’eps’: 0.3, \textbackslash{min\_samples’: 20\} & 1.1568 & 1458 \\  & \{’eps’: 0.3, \textbackslash{min\_samples’: 50\} & 1.2658 & 1589 \\ \hline \multirow{4}{*}{HDBSCAN} & \{’cluster\_selection\_epsilon’: 0.3, \textbackslash{min\_cluster\_size’: 50\} & 0.4475 & 2698 \\  & \{’cluster\_selection\_epsilon’: 0.2, \textbackslash{min\_cluster\_size’: 5\} & 0.7968 & 1398 \\  & \{’cluster\_selection\_epsilon’: 0.3, \textbackslash{min\_cluster\_size’: 30, \textbackslash{min\_samples’: 5\} & 0.9033 & 1548 \\ \hline \multirow{4}{*}{BIRCH} & \{’branching\_factor’: 100, \textbackslash{n\_clusters’: 5, \textbackslash{threshold': 0.4\}} & 1.1823 & 3216 \\  & \{’branching\_factor’: 10, \textbackslash{n\_clusters’: 4, \textbackslash{threshold': 0.3\}} & 1.2641 & 2515 \\  & \{’branching\_factor’: 20, \textbackslash{n\_clusters’: 30, \textbackslash{threshold': 0.4\}} & 1.2951 & 1927 \\ \hline \multirow{4}{*}{Affinity propagation} & \{’damping’: 0.5\} & 1.1374 & 1011 \\  & \{’damping’: 0.8\} & 1.2493 & 1265 \\  & \{’damping’: 0.7\} & 1.2623 & 1255 \\ \hline \multirow{4}{*}{CBMIDE} & \{’n\_components’: 2\} & 1.1731 & 1689 \\  & \{’n\_components’: 30\} & 1.1875 & 1456 \\  & \{’n\_components’: 20\} & 1.2041 & 1265 \\ \hline \multirow{4}{*}{RCBMIDE} & \{’n\_components’: 4\} & 1.0931 & 2035 \\  & \{’n\_components’: 20\} & 1.1175 & 1689 \\  & \{’n\_components’: 10\} & 1.1540 & 1356 \\ \hline \hline \end{tabular} Bold underlined: Best overall model metrics values.  
\end{table} 
Table 2: Davies–Boulding and Calinski–Harabasz metrics values for the top three models of each clustering model evaluated in the research. \"economics\", and other related areas, which can be reasonably interpreted as describing a job profile within the finance/economics domain. In contrast, the second job profile illustrated skills such as \"SQL\", \"programming\", \"testing\", and \"agile\", which are indicative of a programmer's profile. Further elucidation of these job profiles, including additional examples, can be found in Appendix A Figure 11. Based on the compiled profiles, it can be seen that several profiles included skills that were more difficult to understand, such as German, cat, and others (see Profile 8 in Appendix A).