## 5 Conclusions and Future Work  
A new classification algorithm was introduced in this study which was hybridized with clustering to overcome the data distribution problems of classification. By utilizing the special characteristics of the K-means algorithm which changes the data distribution with every iteration, our proposed CCA achieved higher accuracy and has the potential to serve as a stepping stone for better and more efficient computational approaches to deal with datasets of mixed distributions. Four experiments were carried out on two domains' datasets, Phishing URL and Healthcare, in addition to one synthetic dataset in the linear classification case. The results show that the use of clustering increased the accuracy of the model, as each datum has an appropriate number of clusters that can achieve better results in addition to increasing the probability of obtaining a higher accuracy by increasing the number of iterations for K-means. All algorithms showed perfect results in the case of linear classification represented in the first experiment, while their accuracies differed in the other four experiments with mixed distributions. It can be said that the proposed \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Performance Matrix** & **CCA with 0 Cluster** & **CCA with 2 Cluster** & **CCA with 3 Cluster** \\ \hline \multirow{3}{*}{**1**} & Precision & **76.24** & 75.73 & 80.22 & 76.35 \\  & Recall & 77.01 & 76.94 & 80.17 & 77.20 \\  & F1 score & 76.62 & 75.33 & 80.17 & 76.77 \\ \hline \multirow{3}{*}{2} & Precision & 59.22 & 50.54 & 53.85 & 62.87 \\  & Recall & 62.05 & 50.7 & 55.00 & 65.63 \\  & F1 score & 60.60 & 50.62 & 54.42 & 64.22 \\ \hline \multirow{3}{*}{3} & Precision & 55.59 & 58.18 & 62.00 & 59.07 \\  & Recall & 55.68 & 58.24 & 62.08 & 59.08 \\  & F1 score & 55.64 & 58.21 & 62.04 & 59.07 \\ \hline \multirow{3}{*}{4} & Precision & 54.19 & 59.94 & 59.76 & 52.89 \\  & Recall & 52.28 & 56.34 & 55.93 & 51.52 \\ \cline{1-1}  & F1 score & 53.20 & 58.08 & 57.78 & 52.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of F1-Score, Precision, and Recall for CCA. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **RF** & **SVM** & **DT** & **CCA** & **High Accuracy** \\ \hline
1 & 100\% & 100\% & 100\% & 100\% & all \\
2 & 97.3\% & 93.1\% & 96.5\% & 84.0\% & RF \\
3 & 73.6\% & 71.3\% & 69.5\% & 63.2\% & RF \\
4 & 71.3\% & 64.3\% & 64.7\% & 61.8\% & RF \\
5 & 84.2\% & 84.7\% & 75.5\% & 77.2\% & SVM \\ \hline Avg & 85.4\% & 82.7\% & 81.2\% & 77.2\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparing CCA with other three classification algorithms. algorithm is an optimized version of the KNN algorithm because it eliminates the high variability of results due to the changing values of k parameter. In addition, in terms of the dataset, there is a missing values problem where there are many methods that deal with that, such as data mining technique and [34; 35; 36], to the use of other statistical measures such as One-Way ANOVA instead of utilizing Euclidean distance to express similarity between the test points and the Core of each cluster. Furthermore, the limitation of the algorithm is calculated in terms of large overlapping in the dataset, and also the receptivity to concept of clustering should be valid in terms of applying clustering as it shown in the CDC algorithm [20]. For the future, we propose that an approach similar to the GCC [20] algorithm, by taking an active set of points instead of a single Core, will lead to a significant increase in classification accuracy. Using other clustering algorithms such as DBscan, K-SCC, or CDC that can identify the appropriate number of clusters [37] based on the nature of dataset distribution, it will lead to higher model accuracy, as well. Data curation, A.A.; Formal analysis, A.A.; Investigation, A.A.; Methodology, A.A.; Project administration, Z.A. ; Software, A.A.; Supervision, Z.A. ; Writing--original draft, A.A.; Writing--review & editing, Z.A.