AI RESEARCH
CADM: Cluster-customized Adaptive Distance Metric for Categorical Data Clustering
arXiv CS.LG
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ArXi:2511.05826v2 Announce Type: replace An appropriate distance metric is crucial for categorical data clustering, as the distance between categorical data cannot be directly calculated. However, the distances between attribute values usually vary in different clusters induced by their different distributions, which has not been taken into account, thus leading to unreasonable distance measurement.