In this thesis, we will present a method for automated extraction of multivariate, multi-term power functions from large, real data sets. We propose several modifications for the hybrid rule extraction/equation discovery system RF5 to make it applicable for large problems, and present a pruning algorithm to strongly increase the comprehensibility of the found formulas. Elwin Oost, Amsterdam, Au...

In e-discovery, search technology is often used to find documents that are responsive to a request for production, or to identify documents that may be withheld on the basis of attorney-client privilege or work-product protection. In choosing whether or not to employ a particular search methodology, counsel will typically be influenced by various incentives, rules, and ethical considerations, w...

This article describes our experiences in creating a fully integrated, globally accessible, automated chemical synthesis laboratory. The goal of the project was to establish a fully integrated automated synthesis solution that was initially focused on minimizing the burden of repetitive, routine, rules-based operations that characterize more established chemistry workflows. The architecture was...

Despite the recent interest in the neuroanatomy of inductive reasoning processes, the regional specificity within prefrontal cortex (PFC) for the different mechanisms involved in induction tasks remains to be determined. In this study, we used fMRI to investigate the contribution of PFC regions to rule acquisition (rule search and rule discovery) and rule following. Twenty-six healthy young adu...

Numerous measures are used for performance evaluation in machine learning. In predictive knowledge discovery, the most frequently used measure is classification accuracy. With new tasks being addressed in knowledge discovery, new measures appear. In descriptive knowledge discovery, where induced rules are not primarily intended for classification, new measures used are novelty in clausal and su...

Text mining concerns the discovery of knowledge from unstructured textual data. One important task is the discovery of rules that relate specific words and phrases. Although existing methods for this task learn traditional logical rules, soft-matching methods that utilize word-frequency information generally work better for textual data. This paper presents a rule induction system, TEXTRISE, th...

Association rule discovery has emerged as an important problem in knowledge discovery and data mining. The association mining task consists of identifying the frequent item sets, and then forming conditional implication rules among them. Efficient algorithms to discover frequent patterns are crucial in data mining research. Finding frequent item sets is computationally the most expensive step i...

This experiment compared the performance with explicit (rule-application and rule-discovery) and implicit (nonrule-instructed) learning approaches on the performance of a probabilistic video game task requiring fine motor control. The task required visual tracking of a small ball of light and "catching" it by means of joystick manipulation. A general pattern of improvement with practice occurre...