Clustering categorical data: An approach based on dynamical systems

Clustering categorical data: An approach based on dynamical systems (1998)

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by David Gibson , Jon Kleinberg

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@INPROCEEDINGS{Gibson98clusteringcategorical,
    author = {David Gibson and Jon Kleinberg},
    title = {Clustering categorical data: An approach based on dynamical systems},
    booktitle = {},
    year = {1998},
    pages = {311--322}
}

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Abstract

We describe a novel approach for clustering col-lections 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 prod-ucts 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 mea-sure arising from the co-occurrence of values in the dataset. Our techniques can be studied an-alytically in terms of certain types of non-linear dynamical systems. We discuss experiments on a variety of tables of synthetic and real data; we find that our iterative methods converge quickly to prominently correlated values of various cate-gorical fields. 1

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