# Adaptive Hierarchical Density-Based Spatial Clustering Algorithm for Streaming Applications
## 1 Introduction  
In recent years, the number of persons accessing public services has expanded by multiple factors. This is simply due to rising urbanization, in which more people desire to live in cities and, as a result, more cities with urban amenities such as subways are being built. Because the number of people utilizing the infrastructure has risen, while the buildings and infrastructure were created much earlier, this reality has compounded the demand for better safety in public spaces. To address this issue, it has become increasingly vital to ensure that these areas are secure; some examples include subway platforms that are fully isolated from the subway lines save for the entrances. Greater monitoring of the quantity of individuals moving into the platform area can be utilized to increase the efficacy of the method [1].  
Surveillance technology has become increasingly important in enhancing public safety. This is especially true in settings where people are continually congregating, such as parks, stadiums, subways, supermarkets, music halls, and airports. The sheer volume of people traveling through at all hours of the day is enormous. Movements of items can be readily tracked to guarantee safety thanks to significant developments in deep learning objectidentification techniques such as Faster Region-based Convolutional Neural Network (R-CNN) and You only look once (YOLO) [2]. This, however, would necessitate staff in charge of reporting overcrowding. An algorithm that detects people's patterns and clusters would be a good addition to the present technology for improving the efficiency of the whole pipeline. Because we can simply adapt the method to numerous cameras, it would be a very scalable approach [3].  
Clustering techniques are commonly employed in research to tackle data mining challenges when accurate labels are unavailable. It is one of a number of unsupervised learning approaches in which the algorithm learns patterns from a dataset. This is a good strategy for crowd movements because the actual clusters are not known ahead of time. Crowd movements are also characterized by their randomness, with no particular shape density. To ensure that the clusters are not ignored, a robust method is required. HDBSCAN is a clustering algorithm that uses an unsupervised learning algorithm. It was created by [PERSON], [PERSON], and [PERSON] [4], and it extends the Density-Based Spatial Clustering Application with Noise (DBSCAN) technique by adding a hierarchical component that allows flat clusters to be extracted from the tree. The HDBSCAN method is a good unsupervised learning technique that can handle data of various densities and shapes. Because the previous distribution of the data is not known, it is critical that the method can handle data with these distributions. In addition, when running the clustering process, HDBSCAN places a greater focus on the spatial component of the points.  
The rest of the paper continues as follows. Section 2 gives an overview of the HDBSCAN process' progress, followed by the first steps in constructing moving points. The Adaptive HDBSCAN technique is presented in Section 3 along with implementation details. Section 4 deals with the evaluation of the framework proposed, while Section 5 wraps up the study and suggests future research directions.