Soumen Chakrabarti, Byron Dom, et al.
VLDB Journal
We describe a novel approach for clustering collections of sets, and its application to the analysis and mining of categorical data. By "categorical data," we mean tables with fields that cannot be naturally ordered by a metric - e.g., the names of producers of automobiles, or the names of products offered by a manufacturer. Our approach is based on an iterative method for assigning and propagating weights on the categorical values in a table; this facilitates a type of similarity measure arising from the co-occurrence of values in the dataset. Our techniques can be studied analytically in terms of certain types of non-linear dynamical systems.
Soumen Chakrabarti, Byron Dom, et al.
VLDB Journal
Daniel Gruhl, Laurent Chavet, et al.
IBM Systems Journal
Andrei Z. Broder, Anna Karlin, et al.
SODA 1991
Andreas Arning, Rakesh Agrawal, et al.
KDD 1996