K*-Means: A Parameter-free Clustering Algorithm
This addresses the parameter specification issue in clustering for machine learning practitioners, though it is incremental as it builds on k-means with an automated approach.
The paper tackles the problem of clustering without needing to specify the number of clusters, introducing k*-means which automatically determines the optimal number using the minimum description length principle, and it significantly outperforms existing methods when k is unknown.
Clustering is a widely used and powerful machine learning technique, but its effectiveness is often limited by the need to specify the number of clusters, k, or by relying on thresholds that implicitly determine k. We introduce k*-means, a novel clustering algorithm that eliminates the need to set k or any other parameters. Instead, it uses the minimum description length principle to automatically determine the optimal number of clusters, k*, by splitting and merging clusters while also optimising the standard k-means objective. We prove that k*-means is guaranteed to converge and demonstrate experimentally that it significantly outperforms existing methods in scenarios where k is unknown. We also show that it is accurate in estimating k, and that empirically its runtime is competitive with existing methods, and scales well with dataset size.