A statistical technique used in factor, cluster, and regression modelling, where constructs or latent classes are identified from multivariate categorical data and used for further analysis. The probability that a case belongs to a particular latent class is calculated using the maximum likelihood method. The resulting models can also be described as finite mixture models.
Published in Chapter:
Identifying Patterns in Fresh Produce Purchases: The Application of Machine Learning Techniques
Timofei Bogomolov (University of South Australia, Australia), Malgorzata W. Korolkiewicz (University of South Australia, Australia), and Svetlana Bogomolova (Business School, Ehrenberg-Bass Institute, University of South Australia, Australia)
Copyright: © 2020
|Pages: 31
DOI: 10.4018/978-1-7998-0106-1.ch018
Abstract
In this chapter, machine learning techniques are applied to examine consumer food choices, specifically purchasing patterns in relation to fresh fruit and vegetables. This product category contributes some of the highest profit margins for supermarkets, making understanding consumer choices in that category important not just for health but also economic reasons. Several unsupervised and supervised machine learning techniques, including hierarchical clustering, latent class analysis, linear regression, artificial neural networks, and deep learning neural networks, are illustrated using Nielsen Consumer Panel Dataset, a large and high-quality source of information on consumer purchases in the United States. The main finding from the clustering analysis is that households who buy less fresh produce are those with children – an important insight with significant public health implications. The main outcome from predictive modelling of spending on fresh fruit and vegetables is that contrary to expectations, neural networks failed to outperform a linear regression model.