Clustering categorical data: an approach based on dynamical systems

Clustering categorical data: an approach based on dynamical systems 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Clustering categorical data: an approach based on dynamical systems

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Publisher
Springer-Verlag
Copyright
Copyright © 2000 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s007780050005
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

The VLDB JournalSpringer Journals

Published: Feb 1, 2000

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