Summary: We apply multiple machine learning methods to obtain concise rules that are highly predictive of scientifically meaningful classes in hydrating plaster over multiple time periods. We use three dimensional data obtained through X-ray microtomography at greater than one micron resolution per voxel at five times in the hydration process: powder, after 4 hours, 7 hours, 15.5 hours, and after 6 days of hydration. Using statistics based on locality, we create vectors containing eight attributes for subsets of size \(100^3\) of the data and use the autoclass unsupervised classification system to label the attribute vectors into three separate classes. Following this, we use the C5 decision tree software to separate the three classes into two parts: class 0 and 1, and class 0 and 2. We use our locally developed procedural genetic programming system, GPP, to create simple rules for these. The resulting collection of simple rules are tested on a separate \(100^{3}\) subset of the plaster datasets that had been labeled with their autoclass predictions. The rules were found to have both high sensitivity and high positive predictive value.
The classes accurately identify important structural comonents in the hydrating plaster. Morover, the rules identify the center of the local distribution as a critical factor in separating the classes.
For the entire collection see [Zbl 1011.00038].