Learnable Weighting of Intra-Attribute Distances for Categorical Data Clustering with Nominal and Ordinal Attributes
arXiv:2607.05464v1 Announce Type: cross 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
The continuous research in AI, particularly machine learning and data processing, naturally leads to attempts to refine foundational algorithms for better performance in specific data types like categorical data.
Improved clustering methods for categorical data can enhance the accuracy and efficiency of numerous data-driven applications built on machine learning.
This paper proposes a more nuanced approach to handling nominal and ordinal attributes in clustering, potentially leading to more effective data analysis in fields where such distinctions are crucial.
- · Data scientists
- · Machine learning researchers
- · Industries relying on categorical data analysis
- · Legacy clustering methods
More accurate segmentation and pattern recognition in complex datasets, especially those with mixed categorical data.
Enhanced performance in AI applications that depend on unsupervised learning and data grouping, such as anomaly detection or customer segmentation.
Potentially enables new forms of data analysis previously hindered by the limitations of existing categorical data clustering techniques.
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Read at arXiv cs.AI