Susan P. Imberman

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KDD (Knowledge Discovery in Databases) is the automated discovery of patterns and relationships in large databases. Data mining is one step in the KDD process. Many data mining algorithms and methods find data patterns using techniques such as neural Analyzer is a data mining method that finds dependency rules of the form X ⇒ Y. Data is Booleanized with(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)
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)
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)
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)
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. 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.
The use of new technologies, such as RFID sensors, provides scientists with novel ways of doing experimental research. As scientists become more technologically savvy and use these techniques, the traditional approaches to data analysis fail given the huge amounts of data produced by these methods. In this paper we describe an experiment in which colonies(More)