Abstract:
The distance metric that measures object dissimilarity is crucial to categorical data clustering. Most clustering methods ignore the relative order of ordinal values when calculating dissimilarity between nominal and ordinal attributes. Interdependence between nominal and ordinal attributes is worth exploring to show dissimilarity. Thus, this paper will examine the intrinsic difference and relationship of nominal and ordinal attribute values from a graph perspective. Thus, we propose a unified distance metric to measure intra-attribute distances of nominal and ordinal attributes while preserving the order relationship among ordinal values. We then propose a new clustering algorithm to learn intra-attribute distance weights and data object partitions in a single learning paradigm, avoiding a suboptimal solution. Experiments demonstrate the algorithm’s superiority over existing ones.
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