## 5 Conclusions and Discussion  
In an era of emerging AI, where data analysis is pivotal, the ability to select relevant features for specific use cases within a company or organization can significantly impact the outcomes of AI processes. Specifically, as presented in the paper, constructing a measurement matrix for an unsupervised learning dataset enables us to assess the effects of feature reduction on the dataset. The novel methods outlined in this paper, designed, executed, and tested across 13 distinct datasets, showcase the following advantages:  
1. Enable the \"transfer\" of an unsupervised dataset/use-case to a supervised dataset with a target feature, while still retaining relevance to unsupervised functionalities, such as clustering methods. 2. Enable users to define stopping conditions based on the specific requirements and needs of their company or organization. The objective is for users to determine the appropriate termination point for the method to ensure it meets their criteria effectively. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & \begin{tabular}{l} **KNN** \\ **Accuracy** \\ \end{tabular} & \begin{tabular}{l} **KNN** \\ **Precision** \\ \end{tabular} & \begin{tabular}{l} **KNN** \\ **Recall** \\ \end{tabular} & \begin{tabular}{l} **KNN F1** \\ **Accuracy** \\ \end{tabular} & \begin{tabular}{l} **RF** \\ **Precision** \\ \end{tabular} & \
\begin{tabular}{l} **RF Recall** \\ \end{tabular} &
\begin{tabular}{l} **RF F1** \\ **RF F1** \\ \end{tabular} \\ \hline WineQualitywhite & 0.031021 & 0.030409 & 0.031021 & 0.030749 & 0.022041 & 0.022174 & 0.022041 & 0.022152 \\ \hline gas\_turbine\_co\_max\_emission & 0.01764 & 0.017672 & 0.01764 & 0.017664 & 0.011433 & 0.011431 & 0.011433 & 0.010773 \\ \hline Waveform Database & 0.008 & 0.007899 & 0.008 & 0.00832 & 0.0088 & 0.007663 & 0.0088 & 0.00903 \\ \hline USCensusData & 0.01172 & 0.011742 & 0.01172 & 0.011742 & 0.00604 & 0.006024 & 0.00604 & 0.006044 \\ \hline Letter Recognition & 0.0162 & 0.016027 & 0.0162 & 0.016109 & 0.0242 & 0.024087 & 0.0242 & 0.024214 \\ \hline Obesity Levels & 0.011364 & 0.011971 & 0.011364 & 0.011451 & 0.017045 & 0.016996 & 0.017045 & 0.017131 \\ \hline TaiwaneseBankruptcy & 0.027566 & 0.027545 & 0.027566 & 0.027669 & 0.065102 & 0.06443 & 0.065102 & 0.065181 \\ \hline HTRU2 & 0.016983 & 0.016999 & 0.016983 & 0.017004 & 0.013184 & 0.013198 & 0.013184 & 0.013207 \\ \hline Image Segmentation & 0.037736 & 0.037093 & 0.037736 & 0.037628 & 0 & 0.004596 & 0 & \(-\)0.0015 \\ \hline Anuran Calls & \(-0.00334\) & \(-0.00355\) & \(-0.00334\) & \(-0.0034\) & \(-0.00556\) & \(-0.0057\) & \(-0.00556\) & \(-0.0056\) \\ \hline WineQualityRed & \(-0.005\) & \(-0.00345\) & \(-0.005\) & \(-0.00497\) & 0.0075 & 0.007755 & 0.0075 & 0.007497 \\ \hline online\_shoppers\_purchasing & 0 & \(-2\times 10^{-6}\) & 0 & \(-1\times 10^{-6}\) & 0 & 0 & 0 & \(1\times 10^{-6}\) \\ \hline Travel Reviews & \(-0.01633\) & \(-0.01627\) & \(-0.01633\) & \(-0.01649\) & 0.008164 & 0.007436 & 0.008164 & 0.008081 \\ \hline \hline \end{tabular} Note that in the case of the “online\_shoppers\_purchasing” dataset (which counts among the 4 datasets that were reduced), there were no changes in the results in most of the measurements.  
\end{table} 
Table 5: Results analysis. Demonstrate the capability to analyze the impact of each feature by considering the ratio of its distance measure and the total dataset distance measure through unsupervised clustering (that is, using the ratio of the feature \"cost\" with the total cost.). * 4. A significant advantage of the proposed method is its agnosticism, making it effectively applicable to both supervised and unsupervised use cases. This agnostic nature is demonstrated through the research analysis conducted on distinct unsupervised datasets, such as the US Census Data (1990) and the Gas Turbine CO and NOx Emission [55], which are clustering-oriented and lack classification or target features.