## 5 Experimental Studies and Tests
### Results
The explanation for this outcome is that when the number of clusters in any entity increases, the number of levels in the tree decreases. Thus, to reach the cluster where a new job posting is located, it is necessary to compare the number of clusters in each entity from the root. No matter how low the level is to reach the leaves, there will be a performance loss, which is relative to the number of clusters, for the comparison to find the closest cluster in each entity. For example, let the total number of clusters be 16. If we create the tree in a binary structure, the number of levels will be \(log_{2}^{16}=4\). This means that we must carry out \(2\times 4=8\) comparison operations. If we create the tree in a triple structure, the number of levels will be \(log_{3}^{16}=2.5237\). This means that we must carry out \(3\times 2.5237=7.5711\) comparison operations. If we create the tree with a structure of 4, the number of levels will be \(log_{4}^{16}=2\). This means the \(4\times 2=8\) comparison operations must be carried out. In other tests, it was expected that the number of clusters would not have affected the performance obtained by the S-CF Tree algorithm. As detailed in Table 2, complexity analyses were performed for five different job groups, again preserving all features without the FP-Growth algorithm. The analyses were performed by measuring the number of hops (for tree structures) or comparisons (for cluster patterns) required to find job postings close to a new job posting. As there was no clustering model, DQ could not be evaluated according to the number of hops. In the other methods, it was again observed that the hops were proportionally distributed according to the size of the dataset. Moreover, due to the advantageous nature of the tree structure for the search operation, the BIRCH and S-CF Tree algorithms had the lowest hop counts. In Figure 5, SOM patterns for all datasets are shown. The coding for this was performed using the R programming language. The outputs were obtained in hexagonal structure to supply the semantic network structure. The densities of instances in the clusters are indicated by different tones of gray. These patterns had the number of clusters set according to the optimal SSE values for all datasets. The patterns show that the counts of instances in each cluster were distributed nearly homogeneously, with only a few clusters presenting small agglomerations. This even distribution provides a smooth environment in which to search for job postings similar to a new job posting. As shown in Table 3, complexity analyses were performed for five different job groups, again preserving all features without the FP-Growth algorithm. The analyses were performed by measuring the accuracy according to the SSE values. For the accuracy tests of DQs, each job posting was evaluated as a new job posting, and the returned job postings were assumed to be a cluster. The count of job postings illustrated as new job postings was determined as the count of clusters in SOM and \(k\)-Means++ patterns; furthermore, they were selected randomly. Cross-validation was used for the measurements; thus, each job posting could be evaluated. This approach was repeated until the count of clusters in the SOM and \(k\)-Means++ patterns had been obtained separately for each dataset. Finally, cluster patterns were obtained, as in the clustering algorithms, and these approaches could be compared with the clustering algorithms. The SSE values were calculated according to the centroids of all clusters. For the first outcome in the table, it was observed that the SSE values of DQs were the highest. The reason for this is that there was not any categorization operation for the queries associating irrelevant job postings to the new job postings. For