# Nature-Inspired Optimization Algorithms for Text Document Clustering--A Comprehensive Analysis
## 5 Experiments Results and Discussion
### Results and Discussion

Table 4: The results of the Friedman ranking test for the comparative methods using the Accuracy values. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Accuracy** & \multicolumn{8}{c}{**Comparative Algorithms**} \\ \cline{2-9}
**Dataset** & **HS** & **GA** & **PSO** & **ACO** & **KHA** & **CS** & **GWO** & **BA** & **K-Mean** \\ \hline DS1 & 0.521571 & 0.472742 & 0.498831 & 0.541639 & 0.521404 & 0.504682 & 0.622909 & 0.553512 & 0.620902 \\ DS2 & 0.727778 & 0.670421 & 0.725526 & 0.754805 & 0.649999 & 0.677476 & 0.843544 & 0.691741 & 0.659609 \\ DS3 & 0.426878 & 0.356863 & 0.386628 & 0.445097 & 0.425980 & 0.424509 & 0.444046 & 0.454167 & 0.412745 \\ DS4 & 0.4675 In Table 5, the results of the comparative methods using seven datasets (i.e., Harmony Search (HS) Algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) Algorithm, Ant Colony Optimization (ACO), Krill Herd Algorithm (KHA), Cuckoo Search (CS) Algorithm, Gray Wolf Optimizer (GWO), Bat-inspired Algorithm (BA), and K-means technique.) in terms of Precision measure. It is clear that the basic nature-inspired algorithms that have been selected in this experiment have almost the same performance in solving the given text clustering problems. For example, the HS got 0.695420 Precision value in dataset number 2 and 0.421941 in dataset number 7. Also, for another example, the GWO got 0.850940 Precision value in dataset number 2 and 0.401748 in dataset number 3. The results show differences in the outcomes according to the given values in Figure 3.  
The results of the [PERSON] ranking test for the comparative methods (i.e., Harmony Search (HS) Algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) Algorithm, Ant Colony Optimization (ACO), Krill Herd Algorithm (KHA), Cuckoo Search (CS) Algorithm, Gray Wolf Optimizer (GWO), Bat-inspired Algorithm (BA), and K-means technique.) using the Precision values are given in Table 6. In this Table, the rank of each results, summation, mean ranking, and final ranking results. The GWO got the first ranking, followed by ACO, it got the second ranking, BA got the third ranking, K-means got the fourth ranking, PSO got the fifth ranking, CS got the fifth ranking, HS got the seventh ranking, KHA got the eighth ranking, and finally, GA got the ninth ranking. Moreover the summation ranking for the given algorithms using the seventh datasets are given as HS got 43, GA, got 61, PSO got 39, ACO got 20, KHA got 44, CS got 39, GWO got 13, BA got 23, and K-mean got 33. These results can show the significant ability of the nature-inspired optimization algorithms to solve the text clustering problems. In Table 7, the results of the comparative methods using seven datasets (i.e., Harmony Search (HS) Algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) Algorithm, Ant Colony Optimization (ACO), Krill Herd Algorithm (KHA), Cuckoo Search (CS) Algorithm, Gray Wolf Optimizer (GWO), Bat-inspired Algorithm (BA), and K-means technique.) in terms of Recall measure. It is clear that the basic nature-inspired algorithms that have been selected in this experiment have almost the same performance in solving the given text clustering problems. For example, the ACO got 0.737131 Recall value in dataset number 2 and 0.409219 in dataset number 3. Also, for another example, the GA got 0.408991 Recall value in dataset number 5 and 0.428635 in dataset number 6. The results show differences in the outcomes according to the given values in Figure 4. It is clear that the performance of GWO is better than almost all the given nature-inspired optimization algorithms.