## 2 Method
Return the following outputs: the total cost SSE of the K-means algorithm, the number of members in each cluster, confusion matrix measures of the KNN and RF methods, and the number of features remaining in the dataset D.  
The implantation of the above method was conducted via Python 3.12.7 code with the following libraries:  
* Pandas 2.2.2 [44; 45]
* NumPy 1.26.4 [46]
* Sklearn 1.5.1 (few functions) [47]
* Matplotlib 3.9.2 [48]  
Note that the method can be implemented using different code languages. Furthermore, we wish to emphasize that the suggested method specifically addresses dimensionality reduction in unsupervised contexts, regardless of whether the original datasets contained target variables. In our experimental design, we intentionally treated all datasets as unsupervised by omitting any existing class variables. This approach allowed us to evaluate our method's effectiveness in scenarios where no predefined labels exist. Even for datasets that originally contained target variables, we deliberately excluded these labels and generated new cluster-based class assignments through K-means clustering. This design choice aligns with our focus on developing a method that can work effectively without relying on predefined labels, which is particularly valuable in real-world scenarios where labeled data may be unavailable. The key innovation of our approach is precisely this ability to transform an unsupervised problem into a supervised one through clustering, thereby enabling more effective feature selection. In addition, note that the suggested method differs fundamentally from conventional dimensionality reduction techniques in both methodology and objective. Traditional methods like PCA, t-SNE, or UMAP typically transform the feature space while preservingcertain data properties (variance, local structure, etc.). In contrast, our method creates a novel unsupervised learning framework through clustering and then performs feature selection within this framework. The key distinction is that our approach generates labels through K-means clustering and validates them using KNN classification--essentially creating a new supervised task that does not exist in the original (unsupervised) data. This makes direct performance comparisons with traditional dimensionality reduction techniques potentially misleading, as these methods:  
* Do not generate class labels as part of their process
* Are not designed to optimize for the cluster-based classification task we have constructed. * Often transform features rather than select them, which impacts interpretability differently  
Our contribution lies in this novel hybrid approach that bridges unsupervised and supervised learning paradigms for feature selection. The evaluation metric (KNN performance on cluster labels) is intrinsically tied to our methodology rather than being a universal benchmark applicable across all dimensionality reduction techniques. Additionally, we have included Random Forest as another classification method to validate the influence on accuracy and other performance measures after feature removal, not solely relying on KNN. This additional evaluation strengthens our findings regarding the effectiveness of our feature selection approach across different classification paradigms. We also note that the removal of non-numeric features was a methodological choice made to maintain focus on the core contribution of our paper: the hybrid feature selection approach combining unsupervised clustering with supervised learning. The handling of categorical and textual data through various encoding methods, while important, falls outside the scope of our current research objectives.