# Nature-Inspired Optimization Algorithms for Text Document Clustering--A Comprehensive Analysis
## 5 Experiments Results and Discussion
### Results and Discussion
For example, the ACO got 0.388168 Entropy value in dataset number 1 and 0.580499 in dataset number 7. Also, for another example, the KHA got 0.320995 Entropy value in dataset number 1 and 0.625289 in dataset number 7. The results show slight differences in the outcomes according to the given values in Figure 7. It is obvious that the achievement of the used nature-inspired optimization algorithm almost identical and ACO got better results in almost all the given datasets compared to other nature-inspired optimization algorithms. The results of the Friedman 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 Entropy values are given in Table 13. In Table 14, the rank of each results, summation, mean ranking, and final ranking results. The ACO got the first ranking, followed by KHA, it got the second ranking, GWO got the second ranking, K-means got the fourth ranking, BA got the fifth ranking, CS got the sixth ranking, HS got the seventh ranking, GA got the eighth ranking, and finally, PSO got the ninth ranking. Moreover the summation ranking for the given algorithms using the seventh datasets are given as HS got 44, GA, got 48, PSO got 53, ACO got 15, KHA got 27, CS got 36, GWO got 27, BA got 34, 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 Entropy results commit with the other results obtained by the Accuracy, F-measure, Precision, Purity, and Recall measures. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Entropy** & \multicolumn{8}{c}{**Comparative Algorithms**} \\ \cline{2-10}
**Dataset** & **HS** & **GA** & **PSO** & **ACO** & **KHA** & **CS** & **GWO** & **BA** & **K-Mean** \\ \hline DS1 & 0.448296 & 0.465955 & 0.445373 & 0.388168 & 0.320995 & 0.335901 & 0.375879 & 0.421111 & 0.333152 \\ DS2 & 0.352708 & 0.464191 & 0.388575 & 0.324644 & 0.346803 & 0.346968 & 0.239784 & 0.411863 & 0.395838 \\ DS3 & 0.412584 & 0.423777 & 0.408261 & 0.357795 & 0.414517 & 0.419521 & 0.398005 & 0.391949 & 0.333981 \\ DS4 & 0.591471 & 0.575612 & 0.736652 & 0.546981 & 0.583180 & 0.580376 & 0.616758 & 0.619459 & 0.621091 \\ DS5 & 0.498653 & 0.447260 & 0.509601 & 0.440492 & 0.456882 & 0.491119 & 0.514341 & 0.404461 & 0.466960 \\ DS6 & 0.437201 & 0.453595 & 0.501121 & 0.349836 & 0.379381 & 0.407821 & 0.371808 & 0.416385 & 0.387643 \\ DS7 & 0.626637 & 0.641534 & 0.662544 & 0.580499 & 0.625289 & 0.634617 & 0.510802 & 0.609279 & 0.624934 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: The results of the comparative methods in terms of Entropy measure. \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 & 7 & 9 & 8 & 6 & 3 & 4 & 1 & 5 & 2 \\ DS2 & 8 & 9 & 3 & 2 & 4 & 5 & 1 & 6 & 7 \\ DS3 & 7 & 9 & 6 & 4 & 3 & 1 & 2 & 8 & 5 \\ DS4 & 7 & 8 & 9 & 1 & 3 & 5 & 2 & 6 & 4 \\ DS5 & 4 & 2 & 8 & 1 & 7 & 6 & 9 & 3 & 5 \\ DS6 & 8 & 9 & 7 & 2 & 1 & 5 & 4 & 3 & 6 \\ DS7 & 9 & 7 & 8 & 5 & 6 & 3 & 1 & 4 & 2 \\ \hline Summation & 50 & 53 & 49 & 21 & 27 & 29 & 20 & 35 & 31 \\ Mean rank & 7.14285 & 7.57142 & 7.00 & 3.00 & 3.85714 & 4.14285 & 2.85714 & 5.00 & 4.42857 \\ Final ranking & 8 & 9 & 7 & 2 & 3 & 4 & 1 & 6 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: The results of the Friedman ranking test for the comparative methods using the Purity values.