# Nature-Inspired Optimization Algorithms for Text Document Clustering--A Comprehensive Analysis
## 5 Experiments Results and Discussion
### Results and Discussion
Moreover the summation ranking for the given algorithms using the seventh datasets are given as HS got 41, GA, got 61, PSO got 39, ACO got 19, KHA got 45, CS got 39, GWO got 13, BA got 25, and K-mean got 33. These results can show the significant ability of the nature-inspired optimization algorithms to solve the text clustering problems. Moreover, the differences between the obtained results are slight and there is no much variation. The F-measure results commit with the other results obtained by the Precision and Recall measures. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Ranking** & \multicolumn{8}{c}{**Comparative Algorithms**} \\ \cline{2-9}
**Dataset** & **HS** & **GA** & **PSO** & **ACO** & **KHA** & **CS** & **GWO** & **BA** & **K-Mean** \\ \hline DS1 & 4 & 9 & 6 & 5 & 7 & 8 & 1 & 3 & 2 \\ DS2 & 4 & 6 & 3 & 2 & 8 & 7 & 1 & 5 & 9 \\ DS3 & 2 & 9 & 8 & 3 & 5 & 1 & 4 & 6 & 7 \\ DS4 & 8 & 9 & 7 & 2 & 6 & 4 & 1 & 3 & 5 \\ DS5 & 6 & 8 & 5 & 2 & 3 & 7 & 9 & 1 & 4 \\ DS6 & 6 & 9 & 5 & 2 & 7 & 8 & 1 & 3 & 4 \\ DS7 & 8 & 9 & 7 & 4 & 6 & 3 & 1 & 5 & 2 \\ \hline Summation & 38 & 59 & 41 & 20 & 42 & 38 & 18 & 26 & 33 \\ Mean rank & 5.42857 & 8.42857 & 5.85714 & 2.85714 & 6.00 & 5.42857 & 2.57142 & 3.71428 & 4.71428 \\ Final ranking & 5 & 9 & 7 & 2 & 8 & 5 & 1 & 3 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The results of the [PERSON] ranking test for the comparative methods using the Recall values. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**F-Measure** & \multicolumn{8}{c}{**Comparative Algorithms**} \\ \cline{2-9}
**Dataset** & **HS** & **GA** & **PSO** & **ACO** & **KHA** & **CS** & **GWO** & **BA** & **K-Mean** \\ \hline DS1 & 0.526843 & 0.469324 & 0.509023 & 0.529366 & 0.507064 & 0.484753 & 0.637902 & 0.547258 & 0.606673 \\ DS2 & 0.701549 & 0.661178 & 0.722382 & 0.725740 & 0.643889 & 0.661381 & 0.835154 & 0.666718 & 0.625910 \\ DS3 & 0.402158 & 0.336810 & 0.356561 & 0.401984 & 0.389066 & 0.400461 & 0.410225 & 0.395514 & 0.377217 \\ DS4 & 0.452630 & 0.412758 & 0.470815 & 0.536117 & 0.462653 & 0.501924 & 0.548784 & 0.507005 & 0.487956 \\ DS5 & 0.409599 & 0.407297 & 0.438048 & 0.475219 & 0.449388 & 0.413829 & 0.411017 & 0.523684 & 0.440503 \\ DS6 & 0.439249 & 0.417510 & 0.4474198 & 0.484020 & 0.436299 & 0.427266 & 0.519341 & 0.475981 & 0.467066 \\ DS7 & 0.418555 & 0.404324 & 0.422297 & 0.473309 & 0.418632 & 0.484100 & 0.527244 & 0.433272 & 0.496694 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The results of the comparative methods in terms of F-measure measure. In Table 11, 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 Purity 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 0.636698 Purity value in dataset number 1 and 0.542493 in dataset number 7. Also, for another example, the GA got 0.578385 Purity value in dataset number 1 and 0.579877 in dataset number 7. The results show slight differences in the outcomes according to the given values in Figure 6. It is noticeable that the achievement of the used nature-inspired optimization algorithm almost identical and GWO got better results in almost all the given datasets compared to other nature-inspired optimization algorithms.