Graph Based Multi-layer K-means++ (G-MLKM) for Sensory Pattern Analysis in Constrained Spaces

TL;DR

This paper introduces G-MLKM, a novel graph-based multi-layer clustering algorithm designed for unsupervised data-target association in constrained spaces, outperforming traditional probabilistic methods.

Contribution

The paper develops a new graph-based multi-layer K-means++ algorithm for data-target association in constrained spaces, incorporating error correction and graph theory for improved accuracy.

Findings

G-MLKM effectively handles data-target association in constrained environments.

The method outperforms traditional probabilistic approaches in simulations.

Error correction mechanisms enhance clustering accuracy.

Abstract

In this paper, we focus on developing a novel unsupervised machine learning algorithm, named graph based multi-layer k-means++ (G-MLKM), to solve data-target association problem when targets move on a constrained space and minimal information of the targets can be obtained by sensors. Instead of employing the traditional data-target association methods that are based on statistical probabilities, the G-MLKM solves the problem via data clustering. We first will develop the Multi-layer K-means++ (MLKM) method for data-target association at local space given a simplified constrained space situation. Then a p-dual graph is proposed to represent the general constrained space when local spaces are interconnected. Based on the dual graph and graph theory, we then generalize MLKM to G-MLKM by first understanding local data-target association and then extracting cross-local data-target association mathematically analyze the data association at intersections of that space. To exclude potential data-target association errors that disobey physical rules, we also develop error correction mechanisms to further improve the accuracy. Numerous simulation examples are conducted to demonstrate the performance of G-MLKM.