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...
Wedescribea novel approachfor clustering collectionsof sets,andits applicationto theanalysis and mining of categoricaldata. By "categorical data," we meantableswith fiel...
David Gibson, Jon M. Kleinberg, Prabhakar Raghavan
Data clustering methods have many applications in the area of data mining. Traditional clustering algorithms deal with quantitative or categorical data points. However, there exist...
Tadeusz Morzy, Marek Wojciechowski, Maciej Zakrzew...
Bi-clustering is a promising conceptual clustering approach. Within categorical data, it provides a collection of (possibly overlapping) bi-clusters, i.e., linked clusters for both...
Self-organizing maps (SOM) have been recognized as a powerful tool in data exploratoration, especially for the tasks of clustering on high dimensional data. However, clustering on ...
With the growing demand on cluster analysis for categorical data, a handful of categorical clustering algorithms have been developed. Surprisingly, to our knowledge, none has sati...
The discrete nature of categorical data makes it a particular challenge for visualization. Methods that work very well for continuous data are often hardly usable with categorical...
— This paper introduces a quantitative method for social data analysis, which is based on the use of categorical data clustering. More specifically, we employ categorical data cl...
Many real datasets have uncertain categorical attribute values that are only approximately measured or imputed. Uncertainty in categorical data is commonplace in many applications...
Abstract. This paper is devoted to the analysis of career paths and employability. The state-of-the-art on this topic is rather poor in methodologies. Some authors propose distance...