# Nature-Inspired Optimization Algorithms for Text Document Clustering--A Comprehensive Analysis
## 5 Experiments Results and Discussion
### Results and Discussion  
In this section, the results of the comparative methods are given. the comparative methods includes Harmony Search (HS) Algorithm [103], Genetic Algorithm (GA) [104], Particle Swarm Optimization (PSO) Algorithm [105], Ant Colony Optimization (ACO) [106], Krill Herd Algorithm (KHA) [51], Cuckoo Search (CS) Algorithm [107], Gray Wolf Optimizer (GWO) [108], Bat-inspired Algorithm (BA) [109], and K-means technique [110]. \begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**Number of**} & \multirow{2}{*}{**Source**} \\ \cline{2-2} \cline{4-5}  & **Documents** & & **Features** & **Clusters** \\ \hline DS1 & 299 & 1107 & 4 & Technical Reports \\ DS2 & 333 & 2604 & 4 & Web Pages \\ DS3 & 204 & 4252 & 6 & TREC \\ DS4 & 313 & 3745 & 8 & TREC \\ DS5 & 414 & 4879 & 9 & TREC \\ DS6 & 878 & 4537 & 10 & TREC \\ DS7 & 913 & 3100 & 10 & MEDLINE \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of the comparative methods in terms of Accuracy measure. The results are given in terms of Accuracy, Precision, Recall, F-measure, Purity, and Entropy measures. These are common external evaluation measures used in the domain of text mining, especially for evaluating the text clustering methods. In Table 3, 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 Accuracy 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 GA got 0.472742 Accuracy value in dataset number 1 and 0.431106 in dataset number 7. Also, for another example, the KHA got 0.521404 Accuracy value in dataset number 1 and 0.649999 in dataset number 7. The results show differences in the outcomes according to the given values in Figure 2.  
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 Accuracy values are given in Table 4. 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, CS got the fifth ranking, KHA got the sixth ranking, HS got the seventh ranking, PSO 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 42, GA, got 59, PSO got 49, ACO got 18, KHA got 40, CS got 38, GWO got 17, BA got 21, and K-mean got 31. These results can show the significant ability of the nature-inspired optimization algorithms to solve the text clustering problems. \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 & 5 & 9 & 8 & 4 & 6 & 7 & 1 & 3 & 2 \\ DS2 & 3 & 7 & 4 & 2 & 9 & 6 & 1 & 5 & 8 \\ DS3 & 4 & 9 & 8 & 2 & 5 & 6 & 3 & 1 & 7 \\ DS4 & 8 & 9 & 7 & 2 & 6 & 5 & 1 & 3 & 4 \\ DS5 & 6 & 7 & 8 & 2 & 3 & 5 & 9 & 1 & 4 \\ DS6 & 8 & 9 & 7 & 2 & 5 & 6 & 1 & 3 & 4 \\ DS7 & 8 & 9 & 7 & 4 & 6 & 3 & 1 & 5 & 2 \\ \hline Summation & 42 & 59 & 49 & 18 & 40 & 38 & 17 & 21 & 31 \\ Mean rank & 6.00 & 8.42857 & 7.00 & 2.57142 & 5.71428 & 5.42857 & 2.42857 & 3.00 & 4.42857 \\ Final ranking & 7 & 9 & 8 & 2 & 6 & 5 & 1 & 3 & 4 \\ \hline \hline \end{tabular}
\end{table}