Eye-Tracking Data (time series data )Clustering and Analysis

Goal: To develop better statistical methods for modeling individual
differences and assigning individuals to latent groups for time
series data.
The focus lies on identifying latent groups in time series data, such as eye-tracking data.

we hav 12 texts from physics and biology 6 texts each..
We hav 75 readers who are experts in physics and biology

We need to use clustering algorithm to cluster the patterns..for example: get the similarity matrix generation using needle man wunsch...and apply unsupervised techniques like clustering algorithm like ward's, DBSCAN etc., on top of the matrix

To find how are the readers who are experts and amateur in physics and biology are clustered or how are the users clustered does the domain knowledge or the expertise make sense in the clustering

Like how the clusters formed for 12 texts...run the clustering model for every 12 texts and analyze it like how are the clusters formed....does the domain expertise impacts or not etc., as inferences from the Clusters formed

We cannot comment anything on the clustering beforehand as its unsupervised and we cannot draw any inferences beforehand without clustering