A clustering algorithm based on the weighted entropy of conditional attributes for mixed data

A novel definition for weighted entropy is proposed to improve clustering performance for small and diverse datasets. First, intra‐class and inter‐class weighted entropies for categorical and numeric conditional attributes are respectively developed using the mathematical definition of entropy. Second, the weighted entropy is used to calculate cluster weights for mixed conditional attributes. A unique weighted clustering algorithm that adopts entropy as its primary description term, after integrating the corresponding distance calculation mechanism, is then introduced. Finally, a theoretical analysis and validation experiment were conducted using the UC‐Irvine dataset. Results showed that the proposed algorithm offers high self‐adaptability, as its clustering performance was superior to the existing K‐prototypes, SBAC, and OCIL algorithms.