New Clustering Algorithm Unifies Nominal and Ordinal Data Distances.

Categorical data clustering heavily relies on effective distance metrics to measure dissimilarity between objects. However, many existing methods fail to differentiate between nominal and ordinal categorical attributes, neglecting the inherent order information present in ordinal values. Furthermore, interdependencies among these attribute types are often overlooked, which could provide valuable insights into dissimilarity. This research introduces a novel distance metric designed to address these limitations. It measures intra-attribute distances for both nominal and ordinal attributes in a unified manner, crucially preserving the relative order for ordinal values. The approach views the intrinsic differences and connections between attribute values from a graph-like perspective. Building on this metric, the paper proposes a new clustering algorithm. This algorithm integrates the learning of intra-attribute distance weights with the partitioning of data objects into a single, cohesive learning paradigm. This integrated approach avoids suboptimal solutions that arise from separate, sequential steps, and experimental results demonstrate its superior efficacy compared to current state-of-the-art methods.