Symbolic data analysis aims at generalizing some standard statistical data mining methods, such as those developed for classification tasks, to the case of symbolic objects (SOs). ...
Abstract. Clustering data described by categorical attributes is a challenging task in data mining applications. Unlike numerical attributes, it is difficult to define a distance b...
The convergence of embedded sensor systems and stream query processing suggests an important role for database techniques, in managing data that only partially – and often inacc...
Eirinaios Michelakis, Daisy Zhe Wang, Minos N. Gar...
While the vast majority of clustering algorithms are partitional, many real world datasets have inherently overlapping clusters. Several approaches to finding overlapping clusters...
In an interactive classification application, a user may find it more valuable to develop a diagnostic decision support method which can reveal significant classification behavior...