# Nature-Inspired Optimization Algorithms for Text Document Clustering--A Comprehensive Analysis
## 5 Experiments Results and Discussion
### Results and Discussion
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Ranking** & \multicolumn{8}{c}{**Comparative Algorithms**} \\ \cline{2-10}
**Dataset** & **HS** & **GA** & **PSO** & **ACO** & **KHA** & **CS** & **GWO** & **BA** & **K-Mean** \\ \hline DS1 & 5 & 9 & 6 & 4 & 7 & 8 & 1 & 3 & 2 \\ DS2 & 4 & 7 & 3 & 2 & 8 & 6 & 1 & 5 & 9 \\ DS3 & 2 & 9 & 8 & 3 & 6 & 4 & 1 & 5 & 7 \\ DS4 & 8 & 9 & 6 & 2 & 7 & 4 & 1 & 3 & 5 \\ DS5 & 8 & 9 & 5 & 2 & 3 & 6 & 7 & 1 & 4 \\ DS6 & 6 & 9 & 5 & 2 & 7 & 8 & 1 & 3 & 4 \\ DS7 & 8 & 9 & 6 & 4 & 7 & 3 & 1 & 5 & 2 \\ \hline Summation & 41 & 61 & 39 & 19 & 45 & 39 & 13 & 25 & 33 \\ Mean rank & 5.8571429 & 8.71429 & 5.57143 & 2.71429 & 6.42857 & 5.57143 & 1.857143 & 3.57143 & 4.71428 \\ Final ranking & 7 & 9 & 5 & 2 & 8 & 5 & 1 & 3 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The results of the [PERSON] ranking test for the comparative methods using the F-measure values. Figure 5: The F-measure results of the comparative methods using seven datasets. 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 Purity values are given in Table 12. 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, KHA got the third ranking, CS got the fourth ranking, K-means got the fifth ranking, BA got the sixth ranking, PSO got the seventh ranking, HS 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 50, GA, got 53, PSO got 49, ACO got 21, KHA got 27, CS got 29, GWO got 20, BA got 35, and K-mean got 31. These results can reveal the significant ability of the nature-inspired optimization algorithms to address the text clustering problems. Besides, the differences between the achieved results are small, and there is no much difference. The Purity results commit with the other results obtained by the Accuracy, F-measure, Precision, and Recall measures. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Purity** & \multicolumn{8}{c}{**Comparative Algorithms**} \\ \cline{2-10}
**Dataset** & **HS** & **GA** & **PSO** & **ACO** & **KHA** & **CS** & **GWO** & **BA** & **K-Mean** \\ \hline DS1 & 0.636698 & 0.578385 & 0.632088 & 0.687157 & 0.753064 & 0.713239 & 0.781025 & 0.701243 & 0.778676 \\ DS2 & 0.743519 & 0.721757 & 0.806309 & 0.910619 & 0.798183 & 0.789050 & 0.989379 & 0.776432 & 0.754982 \\ DS3 & 0.626638 & 0.572274 & 0.629077 & 0.642075 & 0.667246 & 0.714183 & 0.693792 & 0.622121 & 0.632117 \\ DS4 & 0.630758 & 0.580626 & 0.557168 & 0.788665 & 0.708666 & 0.658809 & 0.718479 & 0.648318 & 0.688434 \\ DS5 & 0.718831 & 0.750700 & 0.661226 & 0.831996 & 0.661298 & 0.664520 & 0.648362 & 0.733289 & 0.683852 \\ DS6 & 0.633277 & 0.557320 & 0.644802 & 0.688866 & 0.729106 & 0.674527 & 0.678664 & 0.680488 & 0.656595 \\ DS7 & 0.542493 & 0.579877 & 0.564461 & 0.644539 & 0.583298 & 0.662628 & 0.750014 & 0.658338 & 0.663414 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The results of the comparative methods in terms of Purity measure. Figure 6: The Purity results of the comparative methods using seven datasets. In Table 12, 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 Entropy measure. It is obvious 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.