Clustering is an important technique for data mining which allows us to discover unknown relationships in our data sets. Clustering algorithms that use metrics based on the natural ordering of numbers cannot be applied to categorical (non-numerical) data. In this tutorial we will review the main methods for numerical data clustering (K-Means, Hierarchical Clustering and Fuzzy CMeans) and then s...

Statistical methods for categorical data, such as loglinear models and logistic regression, represent discrete analogs of the analysis of variance and regression methods for continuous response variables. However. while graphical display techniques are common adjuncts to analysis of variance and regression, methods for plotting contingency table data are not as widely used. This paper provides ...

Statistical methods for categorical data. such as loglinear models and logistic regression, represent discrete analogs of the analysis of variance and regression methods for 'ontinuous response variables. However. while graphical display techniques are common adjuncts to analysis of ~~-variance and regression. methods for plotting contingency table data are not as widely used. This paper provid...

| Visualization provides a means for exploratory analysis of large scale, complex data. In domains such as network management, these data often have categorical attributes, such as host names and event types. Unfortunately, large scale visualiza-tions of categorical data are diicult to construct since categorical values have no inherent order. We consider two visual tasks: nding groups of simil...

Cluster ensembles offer a solution to challenges inherent to clustering arising from its ill-posed nature. In this paper we focus on the design of ensembles for categorical data. Our approach leverages diverse input clusterings discovered in random subspaces. We experimentally demostrate the efficacy of our technique in combination with the categorical clustering algorithm COOLCAT.

Context. The development of effective distance metrics and similarity measures for categorical features is an important task in data analysis, machine learning, decision theory since a significant portion object properties described by non-numerical values. Typically, the dependence between may be more complex than simply comparing them equality or inequality. Such attributes can relatively sim...