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In this paper we present an account of the main features of Snout, an intelligent assistant for exploratory data analysis (EDA) of social science survey data that incorporates a range of data mining techniques. EDA has much in common with existing data mining techniques: its main objective is to help an investigator reach an understanding of the important relationships in a data set rather than simply...
This paper describes the techniques used for categorizing variables in Snout an intelligent assistant for exploratory data analysis of survey and similar data sets that is currently under development. We begin by reviewing existing work on category formation in data mining which has been mainly concerned with enabling decision tree programs to handle numeric variables. It is argued that there are...
In this paper we investigate several methods for producing smaller decision trees by reducing fragmentation through the use of methods that lower the mean branching factor. All the methods considered achieve this goal by grouping the values that each attribute may take. We show how such grouping may be carried out by using either top-down iterative splitting or bottom-up iterative merging. Such methods...
The spinal cord is a vital organ that serves as the only communication link between the brain and the various parts of the body. It is vulnerable to traumatic spinal cord injury and various diseases such as tumors, infections, inflammatory diseases and degenerative diseases. The exact segmentation and localization of the spinal cord are essential to effective clinical management of such conditions...
A convex optimization based source estimation method is presented for dynamic models. The effectiveness of the method is illustrated in the context of a simple atmospheric puff-based dispersion model. Source estimation is the process of inferring the source parameters from the sensor measurements and the physical model. In dispersion, the most important source parameters include the locations and...
When ignorance due to the lack of knowledge, modeled as epistemic uncertainty using Dempster-Shafer structures on closed intervals, is present in the model parameters, a new uncertainty propagation method is necessary to propagate both aleatory and epistemic uncertainty. The new framework proposed here, combines both epistemic and aleatory uncertainty into a second-order uncertainty representation...
The paper builds upon a recent approach to find the approximate bounds of a real function using Polynomial Chaos expansions. Given a function of random variables with compact support probability distributions, the intuition is to quantify the uncertainty in the response using Polynomial Chaos expansion and discard all the information provided about the randomness of the output and extract only the...
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