## 4 Data Analysis and Discussion
### Text Mining Analysis (Clustering)
#### 4.2.1 Clustering Results  
We aim to apply some transformations on each part obtained in Section 4.2. To do this, we consorted all the keywords to lower-case first. Second, we eliminated stop words, numbers, and punctuation in order to improve the analysis results. Third, we adopted tokenizers as regular expressions to keep words with hyphens to ameliorate the semantical interpretability \begin{table}
\begin{tabular}{l l l} \hline \hline
**Rank** & **Keyword** & **Keywords Count** \\ \hline
24 & Genetic algorithm & 75 \\
25 & fuzzy logic & 65 \\
26 & Hippocampus & 64 \\
27 & Prediction & 64 \\
28 & Dimensionality reduction & 59 \\
29 & Exponential stability & 58 \\
30 & Cognition & 58 \\
31 & Artificial intelligence & 57 \\
32 & Adaptive control & 56 \\
33 & Stability & 54 \\
34 & Dementia & 53 \\
35 & Deep brain stimulation & 52 \\
36 & Oxidativesness & 52 \\
37 & Electroencephalogram & 51 \\
38 & Swarm intelligence & 51 \\
39 & Support vector regression & 46 \\
40 & Forecasting & 46 \\
41 & Serotonin & 46 \\
42 & Polymorphism & 46 \\
43 & Linear matrix inequality & 45 \\
44 & Time-varying delay & 45 \\
45 & Reinforcement learning & 44 \\
46 & botulinum toxin & 43 \\
47 & Parkinson’s disease & 42 \\
48 & Adhd & 42 \\
49 & Modeling & 41 \\
50 & Evolutionary algorithm & 41 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._of the analysis. Finally, we verified the correctness of the keywords and revised the ones with minor spelling mistakes and removed the undetectable ones by using NLTK. In preference to using a term-document frequency by keywords, we used TF-IDF vector (Term Frequency-Inverse Document Frequency), which is widely used in text mining research. TF consists of the occurrence number of a keyword in a document. It highlights how important a word is in a document. IDF is obtained by taking the logarithm of the quotient (N/DF), where (N) is the total number of documents and (DF) is the number of documents containing the keyword. It is used to represent the number of occurrences of a keyword in a collection of documents. Consequently, each cluster contains frequent words. Next, we determine the number of clusters (k) through the k-means algorithm. We also adopt trial-and-error to specify the best number of clusters by comparing the obtained k values of clustering results and selecting the most suitable value for the involved dataset. As a consequence, we fix the number of clusters to 10. The clustering model results are presented in Figures 6-8 where 10 clusters were adopted for each time period, and each cluster contains 10 keywords. The obtained results shed light on the research focus during the three time periods of 2007-2011, 2012-2015, and 2016-2019. In the period of 2007-2011, the top terms per cluster suggest a strong research focus on optimization, machine learning, and support vector machines (SVMs). This was a time when researchers were actively exploring various algorithms to improve the efficiency and accuracy of machine-learning models. The use of genetic algorithms and extreme learning machines (ELMs) was also prominent during this period. Moving on to the period of 2012-2015, the top terms per cluster show that the research focus shifted towards deep learning and artificial neural networks. Researchers were exploring the potential of deep neural networks and their applications in image classification and other areas. The use of deep learning algorithms and Recurrent Neural Networks was prominent during this time. Additionally, researchers were also investigating particle swarm optimization and genetic algorithms for improved classification and optimization. In fact, in the period of 2016-2019, the top terms per cluster indicate that researchers were focused on improving the stability and prediction capabilities of machine-learning models. The use of exponential stability, adaptive control, and optimization was prominent during this time.