A New Method for Determining the Number of Clusters Without Clustering

Clustering methods are essential for identifying patterns in data, and the number of clusters significantly impacts the quality of results. Determining the optimal number of clusters is challenging, particularly for large datasets, as traditional methods can be computationally expensive. Developing efficient techniques to determine the number of clusters is crucial for improving both the accuracy and scalability of clustering, especially in large-scale applications. In this study, a new approach for determining the number of clusters is presented. The proposed method aims to find the number of clusters based solely on the distances between data points, without performing clustering. Similar to the Elbow method, the elbow point is found for the distances between data points, and the number of clusters is determined using this elbow point. The proposed algorithm was compared with the Elbow method using 11 real-world datasets and 4 performance metrics. The results demonstrate that the proposed method is particularly advantageous in terms of time complexity, especially as the dataset size increases.