Learnable Weighting of Intra-Attribute Distances for Categorical Data Clustering with Nominal and Ordinal Attributes
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arXiv:2607.05464v1 Announce Type: new 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…
1Key Takeaways
- arXiv:2607.05464v1 Announce Type: new 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….
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3Why it matters
Research breakthroughs often arrive in products months later—early signals matter for strategy. arXiv ML reports that arXiv:2607.05464v1 Announce Type: new Abstract: The success of categorical data clustering generally much relies on the distance metric that measures the dissimilarity degree between two objects.
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