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Subgroup discovery systems are concerned with finding interesting patterns in labeled data. How these systems deal with numeric and nominal data has a large impact on the quality of their results. In this paper, we consider two ways to extend the standard pattern language of subgroup discovery: using conditions that test for interval membership for numeric attributes, and value set membership for...
Clustering is a method to classify given data or objects into groups called clusters using their profiles described by some attributes. In this research, we focus on cluster analysis for nominal data sets in which all attributes are nominal. For objects with nominal attributes, logical or conceptual expressions such as “attribute a equals to v” or “a is not less than v” are suitable to describe natures...
Inductive Logic Programming (ILP) is one of the main approaches to relational learning, with the stronger expressive power and the ease of using background knowledge. However, compared with the traditional attribute-value learning methods, it is much less mature for ILP to deal with imperfect data. This paper applies the Tolerance Rough Set to ILP to further extend the RS-ILP model. We first investigate...
Data sets with a large number of nominal variables, some with high cardinality, are becoming increasingly common and need to be explored. Unfortunately, most existing visual exploration displays are designed to handle numeric variables only. When importing data sets with nominal values into such visualization tools, most solutions to date are rather simplistic. Often, techniques that map nominal values...
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