Qualitative models are often a useful abstraction of the physical world. Learning qualitative models from numerical data sible way to obtain such an abstraction. We present a new ...
Jure Zabkar, Martin Mozina, Ivan Bratko, Janez Dem...
Recently Sarathy and Muralidhar (2009) provided the first attempt at illustrating the implementation of differential privacy for numerical data. In this paper, we attempt to provid...
Simple presentation graphics are intuitive and easy-to-use, but only show highly aggregated data. Bar charts, for example, only show a rather small number of data values and x-y-pl...
Programs such as Bacon, Abacus, Coper, Kepler and others are designed to find functional relationships of scientific significance in numerical data without relying on the deep dom...
Generalization of the covariance concept is discussed for mixed categorical and numerical data. Gini's definition of variance for categorical data gives us a starting point to...
We describe AudioCave, an environment for exploring the impact of spatialising sonified graphs on a set of numerical data comprehension tasks. Its design builds on findings regard...
We present in this paper a modification of Lumer and Faieta’s algorithm for data clustering. This algorithm discovers automatically clusters in numerical data without prior kno...
Abstract. Clustering algorithms for multidimensional numerical data must overcome special difficulties due to the irregularities of data distribution. We present a clustering algo...
The complex phenomena of political science are typically studied using qualitative approach, potentially supported by hypothesisdriven statistical analysis of some numerical data. ...