## 4 Conclusions  
In this paper, we proposed a method to generate movement path data based on data without continuity information of signals collected by Wi-Fi RSSI. We confirmed the applicability of the proposed technology as a technique to reduce the positioning error of indoor location-based services. We proposed a technique for generating movement path data by representing the data points of Wi-Fi fingerprint data as Bounding Boxes, merging and expanding BBs using IoU, and forming clusters of data points based on grid cells. Wi-Fi fingerprint data for indoor environments lack sequence information. Therefore, the input data for learning should consist of continuous datasets rather than isolated RSSI values. To solve this problem, this paper constructs BBs from indoor positioning points of Wi-Fi fingerprint data. Based on this, grid cells are created to divide the dataset area. Adjacency is calculated based on the BBs distributed within each cell, enabling the creation of movement paths. In the previous paper, the cluster size was not constant, the number of clusters was fixed, the shape of the cluster was not standardized, and data points that should not be included in the cluster were included. As a result, a problem was identified where the movement path was inaccurately generated due to clustering errors. In this paper, we have proposed improvements to address these issues. The experimental results utilizing the proposed technique demonstrate that the cluster size is a minimum of 5 m. Consequently, the number of clusters can be adjusted flexibly based on the data point distribution. The clustering process utilizes a cell structure to prevent the inclusion of incorrect data points. As a result, it was verified that the movement path exhibited accuracy and a standardized cluster structure. Through experimentation, it was confirmed that an accurate movement path was established within an indoor positioning environment that necessitates time-series information. In addition, we were able to enhance the reliability of the adjacency calculation by addressing the issue of establishing connections between unrelated data points, which arose from the use of an arbitrary threshold in calculating data point adjacencies. And as a result of verifying the similarity between the Wi-Fi fingerprint data and the building structure from which it was collected, we were able to cluster the data points accurately compared to the unsupervised learning clustering method. In the future, research needs to be expanded to include three-dimensional movement path calculations using a voxel structure for generating paths within a three-dimensional environment. Conceptualization, H.-G.S., D.L., C.-G.H. and C.-P.Y. ; methodology, C.-G.H. and C.-P.Y. ; software, H.-G.S. and C.-P.Y. ; validation, H.-G.S., C.-G.H. and C.-P.Y. ; formal analysis, C.-G.H. and C.-P.Y. ; investigation, D.L. and C.-G.H. ; resources, H.-G.S. ; data curation, D.L. and C.-G.H. ; writing--original draft preparation, H.-G.S. and C.-P.Y; writing--review and editing, C.-G.H. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Path** & **Technique Comparison** & **Accuracy** \\ \hline \multirow{4}{*}{KNN} & \multirow{4}{*}{1} & Find (k = 1) & 92.8 \\  & & Medium (k = 10) & 92.5 \\  & & Coarse (k = 100) & 71.8 \\  & & Layer = 1 & 91.2 \\ KNN & 1 & Layer = 2 & 86.7 \\  & & Layer = 3 & 79.3 \\  & & Path = 1 & 93.5 \\ LSTM & n & Path = 5 & 94 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance comparison of the proposed technique with other models (unit: Accuracy %). * [PERSON] et al. (2014) [PERSON]; [PERSON]; [PERSON]; [PERSON] Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. _arXiv_**2014**, arXiv:1412.3555. * [PERSON] et al. (1999) [PERSON]; [PERSON]; [PERSON] Learning to forget: Continual prediction with LSTM. In Proceedings of the International Conference on Artificial Neural Networks (ICANN), Edinburgh, UK, 7-10 September 1999. * [PERSON] et al.