New Zealand Statistical Association 2024 Conference

Ying Cui

Victoria University of Wellington

Semi-supervised model-based clustering for ordinal response data

This is joint work with Louise McMillan, Ivy Liu

This paper introduces a semi-supervised learning technique for model-based clustering. Our research focus is on applying it to matrices of ordered categorical response data, such as those obtained from surveys with Likert scale responses. We use the proportional odds model, which is popular and widely used for analysing such data, as the model structure. Our proposed technique is designed for analysing datasets that contain both labeled and unlabeled observations from multiple clusters. To evaluate the performance of our proposed model, we conducted a simulation study in which we tested the model from six different scenarios, each with varying combinations and proportions of known and unknown cluster memberships. The fitted models accurately estimate the parameters in most of the designed scenarios, indicating that our technique is effective in clustering partially-labeled data with ordered categorical response variables. To illustrate our approach, we use a real-world dataset from the aquaculture area.