Clustering variables by classical approaches and neural network Boolean factor analysis

In this paper, we compare three methods for grouping of binary variables: neural network Boolean factor analysis, hierarchical clustering, and a linear factor analysis on the mushroom dataset. In contrast to the latter two traditional methods, the advantage of neural network Boolean factor analysis is its ability to reveal overlapping classes in the dataset. It is shown that the mushroom dataset provides a good demonstration of this advantage because it contains both disjunctive and overlapping classes.