Initializingk-means Clustering by Bootstrap and Data Depth

Thek-means algorithm is widely used in various research fields because of its fast convergence to the cost function minima; however, it frequently gets stuck in local optima as it is sensitive to initial conditions. This paper explores a simple, computationally feasible method, which providesk-means with a set of initial seeds to cluster datasets of arbitrary dimensions. Our technique consists of two stages: firstly, we use the original data space to obtain a set of prototypes (cluster centers) by applyingk-means to bootstrap replications of the data and, secondly, we cluster the space of centers, which has tighter (thus easier to separate) groups, and search the deepest point in each assembled cluster using a depth notion. We test this method with simulated and real data, compare it with commonly usedk-means initialization algorithms, and show that it is feasible and more efficient than previous proposals in many situations.

Automated summary

The paper introduces a simple and computationally feasible method to provide initial seeds for the k-means clustering algorithm, effectively addressing its sensitivity to initial conditions. The method has been tested and shown to be more feasible and efficient compared to commonly used initialization algorithms in various situations.