为类别数据聚类设计自适应距离度量，提升聚类效果
CADM: Cluster-customized Adaptive Distance Metric for Categorical Data Clustering
- 根据每个簇中属性分布动态调整距离度量
- 在14个数据集上平均排名接近第一
- 适用于纯类别数据和混合数据类型
合适的距离度量对类别数据聚类至关重要，因为类别数据间的距离无法直接计算。然而，不同簇中属性值之间的距离通常因分布差异而不同，现有方法未予考虑，导致距离度量不合理。为此，我们提出一种簇定制的自适应距离度量方法，能根据各簇内属性分布特性动态更新距离。此外，该方法还扩展至包含数值与类别属性的混合数据。实验表明，所提方法在14个数据集上平均排名接近第一，验证了其有效性。源代码可在 https://anonymous.4open.science/r/CADM-47D8 获取。
原文摘要 · Abstract (English)
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. Therefore, we propose a cluster-customized distance metric for categorical data clustering, which can competitively update distances based on different distributions of attributes in each cluster. In addition, we extend the proposed distance metric to the mixed data that contains both numerical and categorical attributes. Experiments demonstrate the efficacy of the proposed method, i.e., achieving an average ranking of around first in fourteen datasets. The source code is available at https://anonymous.4open.science/r/CADM-47D8
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