Previous research (Chrzan and White 2022) identified algorithmic variable selection techniques that were helpful in identifying the key dimensional components of a known segment structure in the presence of redundancies, cluster imbalance, and masking variables. This paper extends that work to consider the additional challenge of multiple segment structures with different dimensional components. We will explore the impact of different relative structural characteristics on the ability of our algorithms to identify key dimensional components and consequences for segmention solutions.
