## 3 Experiment
### Experimental Results
The number of paths used in the experiment was 1 case without a path, and 3, 5, and 7 cases with paths. In experiments using 1 to 5 paths, the accuracy of location estimation gradually improved, while with 7 paths, the accuracy decreased. This confirmed that the optimal number of paths for location estimation is 5. To validate the performance of the clustering method using the proposed cells in this study, we compared it with K-means clustering. The validation experiment involved clustering areas based on the same dataset and training a model to estimate the location of each area. We set up the same task for further comparison. The number of clusters was determined to be 150 through experiments, in order to create a K-means clustering area of a size similar to the minimum size of the grid cell proposed by our approach, which was then applied in the comparative experiment. To compare the performance of this experiment, we used KNN and FNN models. The comparison results are presented in Table 8, and it was confirmed that the grid cell-based clustering method proposed by our approach exhibited superior accuracy and performance improvements across all models when compared to the K-means clustering method. There is a need to conduct experiments with other models to validate the performance of the proposed technology. However, to utilize continuous time-series information such as path data, LSTM must be employed. As a result, the comparison subject was divided into cases where route information is utilized and cases where it is not. KNN and FNN were utilized as the latter models. In the experiment, grid cells were applied to the proposed technology, and its performance was verified in an experiment with one path, where path information was not used for comparison with other models. Additionally, it was tested in an environment with five paths to confirm the maximum performance of the proposed technology. The accuracy of the experimental results was compared with the performance of the models using a classification problem that predicts the cell to which signal data belongs. When there are five paths, learning was conducted and evaluated as a classification problem, predicting the cell to which the final signal data belongs. The results are displayed in Table 9, confirming that the performance of the LSTM model for the proposed technology surpass \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Technique** & **K-Means Clustering** & **Grid Cell-Based** \\  & **Comparison** & **(Cluster Count 150)** & **Clustering** \\ \hline \multirow{4}{*}{KNN} & Find (k = 1) & 91.8 & 92.8 \\  & Medium (k = 10) & 68.9 & 92.5 \\ \cline{1-1}  & Coarse (k = 100) & 26.5 & 71.8 \\ \cline{1-1}  & Layer = 1 & 88.2 & 91.2 \\ \cline{1-1} \cline{2-4} \multirow{2}{*}{FNN} & Layer = 2 & 79.2 & 86.7 \\ \cline{1-1}  & Layer = 3 & 75.9 & 79.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of Clustering Techniques (unit: Accuracy %). In the experiments for this paper, we used a minimum of 5 m as the threshold in the cell division size problem, because clustering could not be performed with a smaller value. Therefore, the bounding box expansion size was limited to a maximum of 5 m. Moreover, there is a limitation that accumulated Wi-Fi RSSI is required for accurate location estimation.