Susan P. Imberman

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Temporal databases naturally contain a wealth of information that can be unearthed by knowledge discovery and data mining techniques. Discovering association rules in market basket data have been widely studied and many algorithms have been developed. In this study, we examine discovery of association rules in temporal databases. We use the enumeration(More)
In this paper we propose a novel method for teaching neural networks with back propagation in an undergraduate Artificial Intelligence course. The students build a robot whose task is to learn path-following behavior with a neural network. Robots are constructed from standard LEGO® pieces and use the MIT Handy Board as a controller.
Analysis of a clinical head trauma dataset was aided by the use of a new, binary-based data mining technique, termed Boolean analyzer (BA), which finds dependency/association rules. With initial guidance from a domain user or domain expert, the BA algorithm is given one or more metrics to partition the entire dataset. The weighted rules are in the form of(More)
PURPOSE Previous studies of accommodative esotropia have been hampered by bias-prone methods of data collection and analysis and by small sample size. The studies have conflicting conclusions, causing uncertain results. This study aims to determine long-term results of standard treatment of accommodative esotropia and identify predictors of outcome, while(More)
In this article we describe a project for an undergraduate artificial intelligence class. The project teaches neural networks using LEGO® handy board robots. Students construct robots with two motors and two photosensors. Photosensors provide readings that act as inputs for the neural network. Output values power the motors and maintain the robot along(More)
In this paper we propose a novel method for teaching neural networks with back propagation in an undergraduate Artificial Intelligence course. We use an agent based approach in the course, as outlined in the textbook Artificial Intelligence A Modern Approach by Stuart Russell and Peter Norvig [7]. The students build a robot agent whose task is to learn(More)
Finding association rules in data that is naturally binary has been well researched and documented. Finding association rules in numeric/categorical data has not been as easy. Many quantitative algorithms work directly on the numeric data limiting the complexity of the generated rules. In addition, as you create intervals from the numeric data the(More)
The incremental mining of association rules has been shown to be more efficient than rerunning standard association rule algorithms such as Apriori. As each increment is processed, we see the emergence of some itemsets. An itemset that has emerged is one that was small and is large in the current increment. An emergent large itemset is a small itemset that(More)
We describe a robotics assignment for CS1. This assignment has been used at our college since 2002. Recently we have been surveying our students as to whether the lab reinforced the programming concepts taught in the course and if students wanted to see more robotics in future courses. Student responses were positive with respect to both issues.