THE APPROXIMATION ALGORITHM BASED ON SEEDING METHOD FOR FUNCTIONAL k-MEANS PROBLEM

Different from the classical k-means problem, the functional k means problem involves a kind of dynamic data, which is generated by continuous processes. In this paper, we mainly design an O(ln k)-approximation algorithm based on the seeding method for functional k-means problem. Moreover, the numerical experiment presented shows that this algorithm is more efficient than the functional k-means clustering algorithm.

Automated summary

This paper investigates the functional k-means problem and proposes an O(ln k)-approximation algorithm based on the seeding method, which is shown to be more efficient than the functional k-means clustering algorithm through numerical experiments.