Clustering analysis of human navigation trajectories in a visuospatial memory locomotor task using K-Means and hierarchical agglomerative clustering

Abstract

Throughout this study, we employed unsupervised machine learning clustering algorithms, namely K-Means [1] and hierarchical agglomerative clustering (HAC) [2], to explore human locomotion and wayfinding using a VR Magic Carpet (VMC) [3], a table test version known as the Corsi Block Tapping task (CBT) [4]. This variation was carried out in the context of a virtual reality experimental setup. The participants were required to memorize a sequence of target positions projected on the rug and walk to each target figuring in the displayed sequence. the participant’s trajectory was collected and analyzed from a kinematic perspective. An earlier study [5] identified three different categories, but the classification remained ambiguous, implying that they include both kinds of individuals (normal and patients with cognitive spatial impairments). On this basis, we utilized K-Means and HAC to distinguish the navigation behavior of patients from normal individuals, emphasizing the most important discrepancies and then delving deeper to gain more insights.

Analysing Kinship in Severe Acute Respiratory Syndrome Coronavirus 2 DNA Sequences Based on Hierarchical and K-Means Clustering Methods Using Multiple Encoding Vector

“…This method aims to place the data points into several partition to decrease the withincluster sum of squares so that the pairwise squared deviations of points are decrease in the same cluster until the centroids are stable [30]. This method also requires the user to use three parameters: the number of cluster K, cluster initiation, and distance metric [31]. The cluster number is required as the initial cluster center in this process, which is randomly calculated.…”