Identifying the number of clusters for K-Means: A hypersphere density based approach
This addresses a key limitation in clustering for data analysis, though it appears incremental compared to existing elbow-based methods.
The paper tackles the problem of determining the number of clusters for K-Means without prior knowledge, proposing a method based on cluster hypersphere density that yields robust and reliable results.
Application of K-Means algorithm is restricted by the fact that the number of clusters should be known beforehand. Previously suggested methods to solve this problem are either ad hoc or require parametric assumptions and complicated calculations. The proposed method aims to solve this conundrum by considering cluster hypersphere density as the factor to determine the number of clusters in the given dataset. The density is calculated by assuming a hypersphere around the cluster centroid for n-different number of clusters. The calculated values are plotted against their corresponding number of clusters and then the optimum number of clusters is obtained after assaying the elbow region of the graph. The method is simple, easy to comprehend, and provides robust and reliable results.