AL and S Methods: Two Extensions for L-Method

Abstract

With the advent of smart IoT and M2M scenarios it becomes necessary to develop autonomous systems that optimize themselves with minimal human intervention. One possible method to achieve this is through Knee/elbow point estimation. Most of the time these points represent ideal compromises for parameters, methods and algorithms. However, estimating the knee/elbow point in curves is a challenging task. Our focus is on determining the ideal number of clusters autonomously. We analyse and discuss well-known knee/elbow estimators and two extensions based on the theoretical definition. The proposed methods (named AL and S methods) were evaluated against state-of-the-art estimators. The proposed methods are a viable stable solution for knee/elbow estimation.