# Nature-Inspired Optimization Algorithms for Text Document Clustering--A Comprehensive Analysis
## 6 Conclusions and Future Works  
Text clustering is one of the efficient unsupervised learning techniques used to partition a huge number of text documents into a subset of clusters. In which, each cluster contains similar documents and the clusters contain dissimilar text documents. Nature-inspired optimization algorithms have been successfully used to solve various optimization problems, including text document clustering problems. Nature-inspired optimization algorithms demonstrated their achievement in solving different kinds of \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Ranking** & \multicolumn{10}{c}{**Comparative Algorithms**} \\ \cline{2-10}
**Dataset** & **HS** & **GA** & **PSO** & **ACO** & **KHA** & **CS** & **GWO** & **BA** & **K-Mean** \\ \hline DS1 & 8 & 9 & 7 & 5 & 1 & 3 & 4 & 6 & 2 \\ DS2 & 5 & 9 & 6 & 2 & 3 & 4 & 1 & 8 & 7 \\ DS3 & 6 & 9 & 5 & 2 & 7 & 8 & 4 & 3 & 1 \\ DS4 & 5 & 2 & 9 & 1 & 4 & 3 & 6 & 7 & 8 \\ DS5 & 7 & 3 & 8 & 2 & 4 & 6 & 9 & 1 & 5 \\ DS6 & 7 & 8 & 9 & 1 & 3 & 5 & 2 & 6 & 4 \\ DS7 & 6 & 8 & 9 & 2 & 5 & 7 & 1 & 3 & 4 \\ \hline Summation & 44 & 48 & 53 & 15 & 27 & 36 & 27 & 34 & 31 \\ Mean rank & 6.28571 & 6.85714 & 7.57142 & 2.14285 & 3.85714 & 5.14285 & 3.85714 & 4.85714 & 4.42857 \\ Final ranking & 7 & 8 & 9 & 1 & 2 & 6 & 2 & 5 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: The results of the Friedman ranking test for the comparative methods using the Entropy values. Figure 7: The Entropy results of the comparative methods using seven datasets. text clustering problems. However, local optima can be trapped because of its focus on exploration (i.e., global search) instead of exploitation (i.e., local search). This effect may be improved over time, as to how well the sets of rules governing various search algorithms work are better understood. There are two main problems in the text clustering application: the initial cluster centroids and the number of clusters. Parameter tuning will also play a critical role in future studies since the parameters' values and settings govern the algorithm's overall performance. From this discussion, we see that nature-inspired optimization algorithms are robustly feasible for continuing use in machine learning domains. In this paper, a comprehensive review is presented to show the most related nature-inspired algorithms that have been used in solving the text clustering problem. This paper summarizes the most common papers published in the literature until the end of the year 2020. Most of the gathered papers describe the optimization methods that have been used in text clustering applications. Several variants of algorithms, including standard, basic, modified, hybrid methods, and others, are studied. Moreover, comprehensive experiments are conducted and analyzed to show the performance of the common well-know nature-inspired optimization algorithms in solving the text document clustering problems including 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), and Bat-inspired Algorithm (BA). Seven text benchmark datasets are used to validate the performance of the tested algorithms. The results showed that the performance of the well-known nurture-inspired optimization algorithms almost the same with slight differences. Moreover, according to the accuracy measure, GWO is the best and GA is the worst. Also, the GWO is the best, and GA is the worst according to the F-measure. For improvement purposes, new modified versions of the tested algorithms can be proposed and tested to tackle the text clustering problems. Finally, we recommend new future investigation directions on text clustering-based nature-inspired optimization algorithms. The most vital features of these algorithms (i.e., GA, GWO, KHA, PSO, and HSA) might be blended for better overall performance in solving the text clustering problems.