# Leveraging LLMs for Efficient Topic Reviews
## 3 Framework
### Core Components
#### 3.1.3 Clustering  
Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) [48] is a clustering algorithm that extends the capabilities of DBSCAN by introducing a hierarchical approach. Unlike traditional clustering algorithms that require a predefined number of clusters, HDBSCAN is able to discover clusters of varying densities, which makes it particularly effective in dealing with real-world data where cluster densities can vary significantly.  
HDBSCAN works by first constructing a minimum spanning tree of the distance-weighted graph, which represents the data points. It then performs a process known as \"condensed clustering\", where the tree is pruned to remove edges that represent noise, and the remaining tree structure reveals the clusters. This method allows HDBSCAN to model noise explicitly as outliers, meaning that points that do not clearly belong to any cluster are not forced into a cluster. This approach significantly improves the quality of the clustering by ensuring that only coherent, high-density regions of data are grouped together, which is crucial for accurate topic representation in NLP tasks.  
One of the key advantages of HDBSCAN is its ability to produce a soft clustering output, where each data point is assigned a probability of belonging to a cluster. This soft clustering approach is particularly useful in situations where data points may be ambiguously located between clusters, as it provides a more nuanced understanding of the data structure compared to hard clustering methods like k-means. Additionally, HDBSCAN automatically determines the number of clusters based on the data, removing the need for the user to specify this parameter, which can be challenging in complex datasets.  
HDBSCAN is employed in the proposed framework primarily due to its efficiency in identifying structures of varying densities, which is vital for obtaining accurate topic representations from the reduced embeddings produced by UMAP. The combination of UMAP and HDBSCAN allows for the discovery of meaningful clusters in high-dimensional data, where traditional clustering methods might struggle. This is particularly important in NLP tasks, where the semantic relationships between topics can vary in complexity and density.  
The framework also maintains flexibility by allowing the integration of other clustering models as needed. This modularity ensures that the framework can adapt to new developments in clustering techniques, keeping it aligned with the latest trends in NLP and data science.  
In studies conducted by [15; 49], it was shown that reducing the dimensionality of high-dimensional embeddings with UMAP can enhance the performance of clustering algorithms like k-means and HDBSCAN, both in terms of accuracy and runtime. The combination of UMAP for dimensionality reduction and HDBSCAN for clustering has been proven effective across various NLP tasks, as demonstrated in several studies. For instance, ref. [50] highlighted the application of machine learning in improving water infrastructure integrity and quality, where UMAP was used to enhance the performance of clustering techniques. Additionally, in [51], HDBSCAN was used alongside UMAP to cluster short social media texts represented by FastText [52] and bidirectional encoder representations from transformers (BERT) embeddings, revealing trending topics within communities. Similarly, ref. [53] applied this approach to identify arguments in real-world datasets, while [54] used it to analyze tweets related to COVID-19 vaccines. Lastly, ref. [55] leveraged the UMAP-HDBSCAN combination for topic modeling in agricultural instructional sentences.  
This robust combination of UMAP and HDBSCAN within the framework ensures that the clustering process is both efficient and effective, yielding high-quality topic representations essential for advanced NLP tasks.