🤖 AI Summary
Existing clustering methods fail to distinguish the semantic differences between nominal and ordinal attributes, often neglecting the inherent ordering among ordinal values and inter-attribute dependencies, which leads to inaccurate distance measurements. To address this limitation, this work proposes a unified graph-based distance metric framework that explicitly models the intrinsic structures of both attribute types. The approach introduces learnable intra-attribute distance weights and jointly optimizes distance learning with cluster assignment in an end-to-end manner. Notably, it is the first method to simultaneously preserve ordinal information and adaptively adjust attribute weights within a single paradigm. Extensive experiments on multiple real-world datasets demonstrate that the proposed method significantly outperforms state-of-the-art algorithms, confirming its effectiveness and superiority.
📝 Abstract
The success of categorical data clustering generally much relies on the distance metric that measures the dissimilarity degree between two objects. However, most of the existing clustering methods treat the two categorical subtypes, i.e. nominal and ordinal attributes, in the same way when calculating the dissimilarity without considering the relative order information of the ordinal values. Moreover, there would exist interdependence among the nominal and ordinal attributes, which is worth exploring for indicating the dissimilarity. This paper will therefore study the intrinsic difference and connection of nominal and ordinal attribute values from a perspective akin to the graph. Accordingly, we propose a novel distance metric to measure the intra-attribute distances of nominal and ordinal attributes in a unified way, meanwhile preserving the order relationship among ordinal values. Subsequently, we propose a new clustering algorithm to make the learning of intra-attribute distance weights and partitions of data objects into a single learning paradigm rather than two separate steps, whereby circumventing a suboptimal solution. Experiments show the efficacy of the proposed algorithm in comparison with the existing counterparts.