Rule induction for data exploration

This paper describes how the statistical technique of cluster analysis and the machine learning technique of rule induction can be combined to explore a database. The ways in which such an approach alleviates the problems associated with other techniques for data analysis are discussed. We report the results of experiments carried out on a database from the medical diagnosis domain. Finally we describe the future developments which we plan to carry out to build on our current work.

Evidence of a transition from perceptual to category induction in 3- to 9-year-old children

We examined whether inductive reasoning development is better characterized by accounts assuming an early category bias versus an early perceptual bias. We trained 264 children aged 3 to 9 years to categorize novel insects using a rule that directly pitted category membership against appearance. This was followed by an induction task with perceptual distractors at different levels of featural similarity. An additional 52 children were given the same training followed by an induction task with alternative stimuli. Categorization performance was consistently high, however we found a gradual transition from a perceptual bias in our youngest children to a category bias around age 6-7. In addition, children of all ages were equally distracted by higher levels of featural similarity. The transition is unlikely to be due to an increased ability to inhibit perceptual distractors. Instead, we argue that the transition is driven by a fundamental change in children’s understanding of category membership.